Energetic cancer stem cells (e-cscs): a new hyper-metabolic and proliferative tumor cell phenotype, driven by mitochondrial energy

ABSTRACT

This disclosure describes the characteristics of the “energetic” cancer stem cell (e-CSC) phenotype. This distinct sub-population of cancer stem cells (CSCs) has a unique energetic profile compared to bulk CSCs, being more glycolitic, having higher mitochondrial mass and elevated oxidative metabolism. e-CSCs also show an increased capacity to undergo cell cycle progression, enhanced anchorage-independent growth, and ALDH-positivity. The e-CSC phenotype presents new targets for cancer therapeutics, and in particular the anti-oxidant response, mitochondrial energy production, and mitochondrial biogenesis of e-CSCs makes them highly susceptible to mitochondrial inhibitors that target e-CSC anti-oxidant response, mitochondrial energy production, and mitochondrial biogenesis. Gene products for e-CSCs are disclosed, as well as classes of mitochondrial inhibiting therapeutic agents. Also disclosed are methods for identifying and separating e-CSCs front bulk cell populations.

FIELD

The present disclosure relates to cancer therapies, and morespecifically to identifying, separating, and/or eradicating “energetic”cancer stem cells, a sub-population of cancer stem cells that aremetabolically-active, hyper-proliferative, and critically-dependent on a3D micro-environment.

BACKGROUND

Cancer stem cells (CSCs) are tumor-initiating cells (TICs) that areresistant to conventional cancer therapies, such as chemo-therapy andradiation treatment. As a consequence, CSCs are responsible for bothtumor recurrence and distant metastasis, driving treatment failure andpoor clinical outcomes in cancer patients. Therefor, innovativeapproaches are necessary to understand how to tackle the problem ofCSCs. Mechanistically, this may be related to the ability of CSCs tosurvive and thrive under harsh conditions and differentmicro-environments. Because CSCs are an especially small sub-set of thetumor cell population, their metabolic and phenotypic properties haveremained largely uncharacterized, until recently.

Moreover, CSCs are strikingly resilient and highly resistant to cellularstress, which allows them to undergo anchorage-independent growth,especially under conditions of low-attachment. As a consequence, theyform 3D spheroids, which retain the properties of CSCs and stem cellprogenitors. In contrast, when subjected to growth in suspension, most“bulk” cancer cells die, via anoikis—a specialized type of apoptosis. Assuch, the clonal propagation of a single CSC results in the productionof a 3D spheroid and does not involve the self-aggregation of cancercells. Therefore, 3D spheroid formation is a functional mad-out forstemness in epithelial cancer cells and allows one to enrich for apopulation of epithelioid cells with a stem-like phenotype. These 3Dspheroids are also known as mammospheres when they are prepared usingbreast cancer cells, such as MCF7, among others.

Previously, 3D spheroids have been generated from 2 distinct ER(+) cellslines (MCF7 and T47D) and subjected to unbiased label-free proteomicsanalysis. This work started the analysis of the phenotypic behavior ofCSCs at a molecular level. The 3D spheroids were directly compared withmonolayers of these cell lines and processed in parallel. This allowedfor an identification of the proteomic features that are characteristicof the CSC phenotype in 3D spheroids, relative to monolayers. Based onthis molecular analysis, mammospheres were observed to be significantlyenriched in mitochondrial proteins. These mitochondrial-related proteinsincluded molecules involved in beta-oxidation and ketonemetabolism/re-utilization, mitochondrial biogenesis, electron transport.ADP/ATP exchange/transport. CoQ synthesis and ROS production, as well asthe suppression of mitophagy. As such, increased mitochondrial proteinsynthesis or decreased mitophagy could allow the accumulation ofmitochondrial mass in CSCs.

Given the increases in CSCs, mitochondrial mass is being considered as anew metabolic biomarker to purify CSCs. Using this overall approach, ithas been observed that it was possible to significantly enrich CSCactivity using only MitoTracker, as a single marker for both ER(+)(MCF7) and ER(−) (MDA-MB-231) breast cancer cell lines. Remarkably,MitoTracker-high cells were found to be chemo-resistant to Paclitaxel,exhibiting resistance to the Paclitaxel-induced DNA-damage response.

What is needed, however, is a method for identifying and characterizingthe most prominent CSCs based on their metabolic profiles. Further, whatis needed are methods for identifying and separating such metabolically“fit” CSCs from the bulk cell population, for further analysis andresearch. Additionally, what is needed are therapeutic strategies andagents that specifically target the “fittest” CSCs, and eliminatefurther cancer growth, including anchorage-independent growth, tumorrecurrence, and distant metastasis.

BRIEF SUMMARY

This disclosure relates cancer therapies, and more specifically toidentifying, separating, and/or eradicating “energetic” cancer stemcells (or “e-CSCs”), a sub-population of cancer stem cells that aremetabolically-active, hyper-proliferative, and critically-dependent on a3D micro-environment. Under the present approach, a gene signature isprovided for detecting the presence of e-CSCs, predicting tumorrecurrence, and/or predicting metastasis. The present approach alsoprovides methods for purifying and collecting e-CSCs from a sample. Insome embodiments, the present approach allows for treating cancerthrough eradicating e-CSCs (e.g., at least a significant portion ofe-CSCs) in a mass, reducing the likelihood of metastasis and recurrence.In some embodiments, the present approach may be used in combinationwith, and/or to increase the effectiveness of, other therapies.

Cancer stem cells (CSCs) are now believed to be one of the main rootcauses of treatment failure in cancer patients world-wide.Mechanistically, this may be related to the ability of CSCs to surviveand thrive under harsh conditions and different micro-environments. Theinventors proposed the theory that CSCs might become resistant toconventional therapies by “boosting” ATP production using an elevatedmitochondrial OXPHOS metabolism. Consistent with this view, a variety ofmitochondrial inhibitors successfully blocked 3D tumor sphere formation,including i) FDA-approved antibiotics (doxycycline, tigecycline,azithromycin, pyrvinium pamoate, atovaquone, hedaquiline), ii) naturalcompounds (actinonin, CAPE, berberine, brutieridin and melitidin), aswell as iii) experimental compounds (oligomycin and AR-C155858, anMCT1/2 inhibitor), among others.

The inventors identified a diverse metabolic heterogeneity in the CSCpopulation. A flow-cytometry approach was used to metabolicallyfractionate the cancer cell population into “low-energy” and“high-energy” cell sub-populations. For this purpose, auto-fluorescencewas used as an endogenous marker of their energetic state. In thiscontext, auto-fluorescence was attributed to the endogenousflavin-containing metabolites, such as FAD, FMN and riboflavin (VitaminB2). One area that was explored is whether growth in a 2D or 3Dmicro-environment affected their metabolic rate and stem-likeproperties.

The current results provide novel evidence for the existence of an“energetic” CSC phenotype, representing the “fittest” CSCs. Remarkably,these e-CSCs share three qualities: They are i) metabolically-active,ii) hyper-proliferative, and iii) critically-dependent on a 3Dmicro-environment.

This disclosure demonstrates that mitochondrial metabolism drives theanchorage-independent proliferation of CSCs. Two human breast cancercell lines. MCF7 (ER(+)) and MDA-MB-468 (triple-negative), were used asmodel systems. To directly address the issue of metabolic heterogeneityin cancer, a new distinct sub-population of CSCs—“energetic” cancer stemcells (e-CSCs)—were identified and characterized, based solely on theirenergetic profile. This cellular phenotype presents new and valuabletargets for anti-cancer therapeutics.

In a single step, an auto-fluorescent cell sub-population was isolatedbased on its high flavin-content, using flow-cytometry. The cells inthis population were further subjected to a detailed phenotypiccharacterization for e-CSCs. As a result of the characterization, e-CSCswere more glycolytic, with higher mitochondrial mass and showedsignificantly elevated oxidative metabolism. Additionally, e-CSCsdemonstrated an increased capacity to undergo cell cycle progression, aswell as enhanced anchorage-independent growth and ALDH-positivity. Giventhe characterization, e-CSCs are susceptible to mitochondrialinhibitors, such as those described herein. For example, e-CSCs may betargeted by treatments with either i) OXPHOS inhibitors (e.g.,Diphenyleneiodonium chloride, abbreviated DPI) or ii) CDK4/6 inhibitors(e.g., Ribociclib). Also, e-CSCs may be targeted by treatments withmitochondrial inhibitors, such as, for example, mitoriboseins,mitoketoseins, antimitoseins, repurposeins, mitoflavoseins, metformin,tetracycline family members, tigecycline family members, erythromycinfamily members, atovaquone, bedaquiline, vitamin c, stiripentol, caffeicacid phenyl ester (CAPE), and berberine.

Finally, two distinct phenotypic sub-types of e-CSCs have beenidentified, depending on whether they were grown as 2D-monolayers or as3D-spheroids. Remarkably, under 3D anchorage-independent growthconditions, e-CSCs were strictly dependent on oxidative mitochondrialmetabolism. Unbiased proteomics analysis demonstrated the up-regulationof gene products specifically related to the anti-oxidant response,mitochondrial energy production, and mitochondrial biogenesis. Thesegene products may be used as companion biomarkers in detecting andtreating e-CSCs in a cancer, as described more fully below. Further,e-CSCs are vulnerable to mitochondrial inhibiting therapeutic agentsthat disrupt the energetic profile and directly target and eliminate the“fittest” e-CSCs. These results have important implications for usinge-CSCs, especially those derived from 3D-spheroids, i) in tumor tissuebio-banking and ii) as a new cellular platform for drug development.

It should be appreciated that the present approach may be practicedthrough numerous embodiments. For example, some embodiments may take theform of methods for identifying and treating e-CSCs in a cancer. Abiological sample of the cancer may be obtained. This could be, forinstance, tissue from a tumor, blood, urine, saliva, and a metastaticlesion, as non-limiting examples. The expression level(s) of at leastone member of the e-CSC gene signature in the sample may be determined.The e-CSC gene signature described herein includes NQO1, ALDH5A1, TXNRand RRM2. In some embodiments, the expression of each gene in the e-CSCgene signature may be measured. It should be appreciated that expressionlevels may be determined using methods known in the art. The determinedexpression level(s) may be compared to a threshold level for the atleast one member of the e-CSC gene signature. A pharmaceuticallyeffective amount of at least one of an OXPHOS inhibitor and a CDK4/6inhibitor may be administered if the determined level exceeds thethreshold level. For example, a differential expression level may beobtained, using as the threshold data for a population of cancersurvivors that did not experience one or more of distant metastasis,tumor recurrence, and treatment failure. In some embodiments, theadministration may be indicated if the quotient of the determined leveldivided by the threshold level exceeds an amount, such as, for example,about 1.2, or in some embodiments, about 2.0. Measurement error may befactored into this quotient, such as, for example, ±0.05 or ±0.10.

Some embodiments of the present approach may take the form of methodsfor predicting and treating tumor recurrence in a cancer. In someembodiments, the cancer exists in a tumor that has been treated withhormone therapy, such as breast cancer. The cancer may be, for example,a benign lesion, a pre-malignant lesion, a malignant lesion, or ametastatic lesion. A biological sample of the cancer may be obtained,and an assay may be performed to detect the presence of e-CSCs in thebiological sample. A pharmaceutically effective amount of at least oneof an OXPHOS inhibitor and a CDK4/6 inhibitor may be administered ife-CSCs are detected in the biological sample. The assay to detect thepresence of e-CSCs in the biological sample may include determining, orhaving determined, a level of expression in the biological sample of atleast one member of the e-CSC gene signature, comparing the determinedlevel to a threshold level for the at least one member, and classifyingthe biological sample as having e-CSCs present if the determined levelexceeds the threshold level.

Some embodiments may take the form of a method for predicting andtreating metastasis in a cancer. The cancer may be, for example, breastcancer. A biological sample of the cancer may be obtained, an assay maybe performed to detect the presence of e-CSCs in the biological sample,and a pharmaceutically effective amount of at least one of an OXPHOSinhibitor and a CDK4/6 inhibitor may be administered if e-CSCs aredetected in the biological sample. The assay to detect the presence ofe-CSCs in the biological sample may include determining, or havingdetermined, a level of expression in the biological sample of at leastone member of the e-CSC gene signature, comparing the determined levelto a threshold level for the at least one member, and classifying thebiological sample as having e-CSCs present if the determined levelexceeds the threshold level.

Some embodiments may take the form of methods for treating cancer havingone or more e-CSCs. A biological sample of the cancer may be obtained,an assay may be performed to detect the presence of e-CSCs in thebiological sample, and a pharmaceutically effective amount of at leastone of an OXPHOS inhibitor and a CDK4/6 inhibitor may be administered ife-CSCs are detected in the biological sample. The assay to detect thepresence of e-CSCs in the biological sample may include determining, orhaving determined, a level of expression in the biological sample of atleast one member of the e-CSC gene signature, comparing the determinedlevel to a threshold level for the at least one member, and classifyingthe biological sample as having e-CSCs present if the determined levelexceeds the threshold level.

The e-CSC therapeutic agent(s) administered may vary betweenembodiments. For example, in some embodiments the therapeutic agent maybe or include diphenyleneiodonium chloride (DPI. In some embodiments,the therapeutic agent may be or include Ribociclib. Examples of othertherapeutic agents include, but are not limited to, atoravaquone,irinotecan, sorafenib, niclosamide, berberine chloride, Abemaciclib, andPalbociclib. It should be appreciated that in some embodiments, morethan one OXPHOS inhibitor and/or more than one CDK4/6 inhibitor may beused. It should be appreciated that the e-CSC therapeutic agent(s) maybe administered with (e.g., before, concurrently, or in close temporalproximity) other cancer therapies, including hormone therapy, radiationtherapy, photodynamic therapy, chemotherapy, among others. The e-CSCtherapeutic agent(s) may be used to increase the effectiveness ofanother cancer therapy, such as through reducing treatment resistance,increasing sensitivity to a treatment, and/or eradicating e-CSCs thatwould otherwise cause further propagation, metastasis, and/orrecurrence. In some embodiments, a mitochondrial inhibitor may beadministered in with (e.g., before, concurrently, or in close temporalproximity) the e-CSC therapeutic agent(s). Examples of mitochondrialinhibitors include, but are not limited to, a mitoribosein, amitoketosein, a antimitosein, a repurposein, a mitoflavosein, metformin,a tetracycline family member, a tigecycline family member, aerythromycin family member, atovaquone, bedaquiline, vitamin c,stiripentol, caffeic acid phenyl ester (CAPE), and berberine.

Some embodiments of the present approach may take the form of methodsfor identifying and purifying e-CSCs in a sample, such as a biologicalsample (e.g., tumor tissue, blood, etc.). The auto-fluorescent signal ofcells in the sample may be measured, and an upper range of measuredauto-fluorescent signals may be identified. Cells having anauto-fluorescent signal within the upper range of measuredauto-fluorescent signals may be identified. In some embodiments, theupper range of measured auto-fluorescent signals is approximately thetop 5% of measured auto-fluorescent signals, it should be appreciatedthat the upper range may vary, such as, for example, the top 10%, thetop 7%, the top 4%, the top 1%, etc. In some embodiments, the identifiedcells may be sorted and collected. Sorting and collecting may occurthrough, for example, fluorescence-activated cell sorting.

In some embodiments, a single-cell suspension may be formed from thesample, and the auto-fluorescent signal of cells in the sample may bemeasured through the auto-fluorescent signal of cells in the suspension.It should be appreciated that auto-fluorescent signal may be measuredthrough flow cytometry, as is known in the art, and thatauto-fluorescence may be attributed to the endogenous flavin-containingmetabolites, such as FAD. FMN and riboflavin. Some embodiments mayinclude measuring ALDH activity of identified cells. Some embodimentsmay include measuring anchorage-independent growth of the identifiedcells. Some embodiments may include measuring the mitochondrial mass ofthe identified cells. Some embodiments may include measuring theglycolytic and oxidative mitochondrial metabolism of the identifiedcells. Some embodiments may include measuring the cell cycle progressionand proliferative rate of the identified cells. Some embodiments mayinclude measuring the poly-ploidy of the identified cells.

Further embodiments of the present approach may be recognized by thosehaving ordinary skill in the art, having reviewed the following detaileddescription.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a method for detecting, separating, and purifying e-CSCs.

FIGS. 2A-2C relate to refinement and characterization of e-CSCs. FIG. 2Ashows an embodiment of an apparatus for e-CSC refinement andcharacterization, and FIGS. 2B and 2C show demonstrative flow-cytometryresults.

FIG. 3A shows cell cycle profiles for different cell sub-populations ofMCF7.

FIG. 3B shows Hoechst staining results for MCF7-monolayer M-L and M-Hcells. FIG. 3C is a cell cycle bar graph.

FIG. 4A shows ALDH activity for MCF7 cell sub-populations. FIG. 4B showsthe side scatter analysis for the sub-populations. FIG. 4C showsmammosphere assay results, and FIG. 4D shows MitoTracker Deep Redresults.

FIGS. 5A-5D show OCR data for MCF7 cell sub-populations.

FIGS. 6A-6D show ECAR data for same MCF7 cell sub-populations.

FIGS. 7A-C show the results of DPI treatment at various concentrationson M-H cells from MCF7 monolayers.

FIGS. 8A-SC show data demonstrating that e-CSCs (3D) are susceptible totargeting with DPI or Ribociclib.

FIGS. 9A-9D show cell cycle progression data for MDA-MB-468 e-CSCs.

FIGS. 10A-10D show OCR data for MDA-MB-468 cell sub-populations.

FIGS. 11A-11D show ECAR data for same MCF7 cell sub-populations

FIGS. 12A-12I show Kaplan-Meier curves for e-CSCs.

DESCRIPTION

The following description includes the currently contemplated modes ofcurrying out exemplary embodiments of the present approach. Thefollowing description is not to be taken in a limiting sense, and ismade merely for the purpose of illustrating the general principles ofthe invention.

As described herein, the present approach relates to identifying,purifying, and collecting a hyper-proliferative cell sub-population ofbreast CSCs, by using an endogenous marker of energy-metabolism, namely,flavin-derived auto-fluorescence. The present approach may take variousforms, depending on the embodiment. For example, under the presentapproach, a gene signature is provided for detecting the presence ofe-CSCs, predicting tumor recurrence, and/or predicting metastasis. Thepresent approach also allows for purifying and collecting e-CSCs from asample. In some embodiments, the present approach allows for treatingcancer through targeting and/or eradicating e-CSCs in a mass.

In addition to having a hyper-proliferative phenotype, e-CSCs showedprogressive increases in stemness markers (e.g., ALDH activity andmammosphere-forming activity), a highly elevated mitochondrial mass, aswell as increased glycolytic and mitochondrial activity. Moreover, the3D sub-type of e-CSCs is strictly dependent on mitochondria, for cellpropagation. Thus, under the present approach, the anchorage-independentpropagation e-CSCs, derived from 3D-spheroids, may be specificallytargeted with an OXPHOS inhibitor (such as, for example, DPI) to inhibitmitochondrial biogenesis, and/or a CDK4/6 inhibitor (such as, forexample, Ribociclib) to inhibit cell proliferation.

Mechanistically, there are at least 2 different classes of e-CSCs thatare metabolically distinct. As used herein, M-H refers to“monolayer-high” cells, and S-H refers to “spheroid-high” cells. Theclassification depends on whether the cells are grown in a 2D-monolayeror a 3D-spheroid micro-environment. A metabolic-switch occurs, likelyduring the transition from anchorage-dependent to anchorage-independentgrowth. This represents a metabolic shift from a glycolytic to a moreoxidative mitochondrial phenotype. More specifically, in 2D-monolayercultures, and as discussed in more detail below, 100 nM DPI increasedthe number M-H cells by ˜7.5-fold over a 5-day period. In contrast, DPI,at exactly the same concentration, almost completely inhibited3D-mammosphere formation, resulting in a population ofanchorage-independent single live cells that were ˜60% depleted of S-Hcells. Therefore, the same mitochondrial OXPHOS inhibitor (DPI) hadcompletely opposite effects, depending on the 2D vs. 3Dmicro-environment of the e-CSCs. These results experimentally imply thatM-H cell propagation in 2D-monolayers is driven by glycolysis, while thepropagation of S-H cells in 3D-spheroids is driven by mitochondrialOXPHOS. Importantly, this suggests that a critical metabolic-switch isoccurring, between the M-H and S-H CSC phenotypes, specifically alteringtheir metabolic requirements.

This 2D-to-3D transition, or “epithelial-mesenchymal-transition (EMT)”is thought to be a more mesenchymal phenotype. In support of thisnotion. ALDH activity was progressively increased and was at its highestlevels in e-CSCs derived from the 3D-spheroids, nearly 9-fold increased,directly supporting the assertions of the present approach. Importantly,ALDH activity is an established functional biomarker of the EMT and“boosts” the production of energy-rich NAD(P)H.

The identification of this unique, energy-driven, cancer cellsub-population will undoubtedly provide new opportunities for i)bio-banking and ii) new drug screening, as well as iii) theidentification of novel metabolic targets, for the prevention of tumorrecurrence and inhibiting the spread of metastatic disease.

Two human breast cancer cell lines. MCF7 and MDA-MB-468, were used asmodel systems, to dissect the role of metabolic heterogeneity intumorigenesis. Results with MCF7 cells are shown in FIGS. 2-8 . Tables1-6, and results with MDA-MB-468 cells are included in FIGS. 9-11 . MCF7cells are ER(+), while MDA-MB-468 cells are triple-negative.Quantitatively similar results were obtained with both model cell lines.Table 1, below, summarizes cell cycle phase data for cell populations ofMCF7-derived e-CSCs. Averages are shown from 4 independent experiments.Abbreviations used: M-L, monolayer-low; M-H, monolayer-high; S-L,spheroid-low; S-H, spheroid-high.

TABLE 1 MCF7-derived e-CSCs cells demonstrate increased cell cycleprogression. 2D-Monolayers (M) 3D-Spheroids (S) CC-Phase(%) M-L M-H S-LS-H G0/G1 81.25 53.23 61.50 37.32 S-phase 3.92 11.43 6.72 10.60 G2/M8.53 21.23 11.72 32.43 Polyploid 3.71 10.74 9.03 17.13

Table 2, below, shows cell cycle data for cell populations fromMDA-MB-468 e-CSCs. As with the MCF7 e-CSCs, these also cells demonstrateincreased cell cycle progression. Table 3, also below, shows ALDHactivity changes. The averages shown in both Tables 2 and 3 are from atleast 3 independent experiments.

TABLE 2 MDA-MB-468 e-CSCs demonstrate increased cell cycle progression.2D-Monolayers (M) 3D-Spheroids (S) CC-Phase (%) M-L M-H S-L S-H G0/G178.95 51.20 64.05 34.75 S-phase 2.96 12.18 9.03 18.10 G2/M 7.65 23.7313.35 32.89 Polyploid 5.30 9.93 7.47 12.24

TABLE 3 MCF7-derived e-CSCs have increased ALDH activity. 2D-Monolayers(M) 3D-Spheroids (S) M-L M-H S-L S-H 0.52% 1.03% (1.98x) 2.13% (4.09x)4.59% (8.83x)

The next series of analyses determined whether mitochondria may functionas the metabolic “engines” to drive cellular hyper-proliferation in CSCsand, ultimately, anchorage-independent growth, leading to tumorrecurrence and metastasis. The analysis also investigated whether two ormore sub-populations of CSCs exist, depending on whether the cells aregrown as 2D-monolayers or as 3D-spheroids). Cell auto-fluorescence wasused as an endogenous marker of cellular energy metabolism, whichdirectly reflects cellular content of flavin-containing compounds (FAD,FMN and riboflavin (Vitamin B2)), which are all high-energy cellmetabolites.

FIG. 1 shows a method for detecting, separating, and purifying e-CSCsaccording to the present approach. The method is described in connectionwith e-CSCs from MCF7 cells, but it should be appreciated that themethod may be applied to cells from other cancer types. First, at S101,single cell suspensions of MCF7 cells were prepared. Then, at S103, theMCF7 cells were subjected to metabolic fractionation by now cytometry toisolate CSCs. The flow cytometry was based on the endogenousauto-fluorescence (AF) of Flavin adenine dinucleotide/Flavinmononucleotide (FAD/FMN) high-energy metabolites. Thedesignator AF(+)refers to cells having high levels of FAD/FMN. The high (H) and low (L)sub-populations of AF cells were then collected from MCF7 cells at S105,and then grown either as i) 2D-monolayers (S107) or ii) 3D-spheroids(S109) using methods known in the art. The “high-energy” AF(+) cellswere then designated as either e-CSCs (2D) and e-CSCs (3D).

With respect to the data described herein, 2D-monolayers and3D-spheroids were first collected and used to prepare single-cellsuspensions. These suspensions were then subjected to flow-cytometry toisolate cells based on their auto-fluorescent properties. Briefly, the“Low-(L)” and “High-(H)” auto-fluorescent cell sub-populations wereselected by gating, within the auto-fluorescence signal. Only cells withthe least (bottom 5%) or the most (top 5%) auto-fluorescent signal werecollected. Both the “Low” and “High” sub-populations of auto-fluorescentcells, generated from either 2D-monolayers (M-L vs. M-H) or 3D-spheroids(S-L vs. S-H) were then subjected to a detailed phenotypiccharacterization and separation. The M-H (“monolayer-high”) and S-H(“spheroid-high”) cell sub-populations were predicted to be the mostenergetic, based on their high (H) flavin-content.

FIG. 2A shows an example of further e-CSC separation and purificationmethod of the present approach. For the data discussed below, both MCF7cells and MDA-MB-468 cells were used. In this illustration, the cellsgrown as 2D monolayer attached cells 201 and 3D spheroid non-attachedcells 203 were collected and dissociated into a single-cell suspensionbefore flow-cytometry sorting through a SONY SH800 cell sorter 205. Theflow-cytometry results are shown in FIG. 2B; the left column shows livecells, and the right column shows singlets. FIG. 2C is an enlargement ofthe forward-scatter and side-scatter data, and was originally in colorbut has been reduced to gray-scale for this application. Based on theforward-scatter and side-scatter analysis of single cells, highlyauto-fluorescent cells 211 were clearly larger in size, than cells withlow auto-fluorescence 213.

The next several paragraphs describe the characterization of the e-CSCphenotype, particularly with respect to proliferation, stemness, andbioenergetics. The e-CSC capacity for cell proliferation was assessedvia cell cycle progression analysis. Representative cell cycle profiles(for different cell sub-populations of MCF7 are shown in FIG. 3A. Thecell sub-populations in each phase are, from left to right, M-L, M-H,S-L, and S-H. FIG. 3B shows the results of Hoechst staining forMCF7-monolayer M-L and M-H cells, and FIG. 3C shows a cell cycle bargraph.

The M-H cell and S-H cell sub-populations were exceedinglyhyper-proliferative, with a reduction of cells in the G0/G1-phase anddramatic increases in both the S-phase and the G2/M-phase. Also, thenumber of polyploid cells (DNA>2N) was increased considerably in boththe M-H and S-H populations. Overall. S-H cells were the mosthyper-proliferative, with >40% of the cells in S-phase and/or G2/M, and<40% of the cells in the G0/G1-phase of the cell cycle. S-H cells alsohad the largest number of polyploid cells, reaching approximately12-17%, probably due to mitotic catastrophe. In contrast, M-L cells hadthe highest number of cells in the G0/G1-phase of the cell cycle (˜80%)and the lowest number of polyploid cells (˜3-5%). Also, M-L cells showedthe lowest number of cells in S-phase (˜3-4%). These hyper-proliferativeresults with MCF7 cells (see Table 1, above) and MDA-MB-468 cells (seeTable 2, above) arm also consistent with a high-energy phenotype. Giventhis phenotype, the M-H and S-H cells were designated as “energetic”CSCs, also referred to as e-CSCs in this disclosure.

The stem cell characteristics of the M-H and S-H cells were thenassessed. Specifically, ALDH was used as a marker of “stemness” activityto carefully monitor the progressive enrichment of CSCs. The results areshown in FIGS. 4A-4D, and show that e-CSCs have increased “stem-like”features. First, FIG. 4A shows the percentage of ALDH-positive cells foreach of M-L, M-S, S-L and S-H cell sub-populations. FIG. 4B shows theside scatter analysis results for the cell populations. FIGS. 4A and 4Bshow that relative to the cells with the least flavin (M-L), all theother cells showed the progressive enrichment of ALDH activity. Withrespect to ALDH activity, the MCF7 cells with the highest flavin-contentalso have the highest ALDH activity. More specifically. M-H cells (from2D-monolayers) and S-H cells (from 3D-spheroids) showed the largestincreases in ALDH activity, as seen by now-cytometry analysis.Remarkably. M-H cells and S-H cells showed a 2-fold and a near 9-foldenrichment of ALDH-activity, respectively (see Table 3, above). Thestem-like phenotype of M-H and S-H cells were further validated by usingthe mammosphere assay to measure anchorage-independent growth and byquantitatively measuring their mitochondrial mass, with MitoTracker DeepRed.

The mammosphere assay allows for the quantitative measurement ofanchorage-independent growth, which is a functional read-out for“stemness” activity. High mammosphere formation in MCF7 cells directlycorrelates with high-flavin content. For example, M-H cells (from2D-monolayers) and S-H cells (from 3D-spheroids) show the highest ratesof mammosphere formation, as compared to the M-L and S-Lsub-populations. FIG. 4C shows the mammospheres assay results, andillustrates that relative to control cells, the M-H and S-H cellsub-populations formed mammospheres with greater efficiency, ˜1.6-foldand 2.3-fold, respectively.

Mitochondrial mass was assessed using MitoTracker Deep Red vitalstaining. The mitochondrial mass in MCF7 cells correlates withhigh-flavin content. In particular. S-H cells (from 3D-spheroids) showthat largest increases in mitochondrial mass, as seen by flow-cytometrywith MitoTracker Deep Red vital staining. FIG. 4D shows the MitoTrackerDeep Red results, which is synonymous with their mitochondrial status.Relative to M-L cells, M-H cells showed a clear ˜1.45-fold increase inmitochondrial mass. Relative to S-L cells, S-H cells demonstrated aremarkable ˜4-fold increase in mitochondrial mass.

Therefore, e-CSCs derived from 3D-spheroids were i) the mosthyper-proliferative, ii) showed the largest increases in stemnesscharacteristics (ALDH activity and anchorage-independent growth), andiii) had the highest mitochondrial mass. These phenotypic changes arehighly suggestive of metabolic re-programming, especially towards moreoxidative mitochondrial metabolism.

To characterize the bioenergetic phenotype of e-CSCs, the cellpopulations were subjected to metabolic flux analysis, using theSeahorse XFe96. Mitochondrial oxygen consumption rate (OCR) andextracellular acidification rate (ECAR) were the measured properties.OCR results include basal respiration, maximal respiration, and ATP.ECAR results include glycolysis, glycolytic reserve, and glycolyticreserve capacity. FIGS. 5A and 5B show OCR results for M-H and M-Lcells, and FIGS. 5C and 5D show the OCR results for S-H and S-L cells.High OCR in MCF7 cells directly correlates with high-flavin content. Forexample, M-H cells (from 2D-monolayers) and S-H cells (from3D-spheroids) have the highest levels of OCR, as compared to the M-L andS-L sub-populations.

The data also demonstrates that e-CSCs have elevated levels of aerobicglycolysis. The extracellular acidification rate (ECAR) was measured,using the Seahorse XFe96 metabolic-flux analyzer. FIGS. 6A and 6B showECAR results for M-H and M-L cells, and FIGS. 6C and 6D show the ECARresults for S-H and S-L cells. Note that high ECAR in MCF7 cellsdirectly correlates with high-flavin content. For example, M-H cells(from 2D-monolayers) and S-H cells (from 3D-spheroids) have the highestlevels of ECAR, as compared to the M-L and S-L sub-populations. It canbe seen from FIGS. 5A and 5B that M-H cells are highly oxidative, withan almost 2-fold increase in OCR, mitochondrial respiration andATP-production. However, the largest changes can be seen in FIG. 6A, inglycolytic phenotype, with about a 4-fold increase in glycolyticactivity for M-H cells. As such. M-H cells are highly glycolytic, andhave an enhanced mitochondrial metabolism.

In contrast, S-H cells demonstrated the highest increases in OCR, with anear 3-fold increase in basal respiration and a 4-fold increase in ATPproduction as seen in FIG. 5C. However, FIG. 6D shows that the S-Hcells' basal glycolytic rate remained unchanged, suggestive of a greaterdependence on mitochondrial OXPHOS metabolism. As a consequence, S-Hcells are expected to be more sensitive to mitochondrial OXPHOSinhibitors, highlighting a weak point in e-CSCs derived from3D-spheroids.

The e-CSC phenotype allows for developing new therapeutics that targetthe metabolic nature of the sub-population. For example, it should beappreciated that e-CSCs may be targeted using OXPHOS inhibitors and/orCDK4/6 inhibitors. They may also be targeted with mitochondrialinhibitors. The following paragraphs describe examples of suchtherapeutic agents for targeting and eradicating e-CSCs, as ananti-cancer therapy. It should be appreciated that in some embodiments,these therapeutics may be used in conjunction with other anti-cancertherapies.

The first example therapeutic is DPI (Diphenyleneiodonium chloride), anOXPHOS inhibitor that specifically targets flavin-containing enzymes,especially those associated with FMN/FAD and mitochondrial complex I andII. DPI inhibits 3D-spheroid formation in MCF7 cells, and DPIselectively inhibits mitochondrial function without any toxic sideeffects. In prior studies. DPI did not induce changes in cell viabilityor apoptosis, but instead shifted the cells towards a more glycolyticphenotype.

FIGS. 7A-C show the results of DPI treatment at various concentrationson M-H cells from MCF7 monolayers. The data represents 100.000 cellsafter 120 hours of DPI treatment. First, FIG. 7A shows a series of flowcytometry tracings, each at a different concentration of DPI, and showsan increase in M-H cells as DPI concentration increases. FIG. 7B is abar graph showing the change in M-H cells after treatment, alsoconfirming the increase in M-H cells. The bar graph shows that thepercentage of M-H cells are increased after treatment with DPI, amitochondrial OXPHOS inhibitor, over a 5-day period, in a concentrationdependent manner. FIG. 7C shows MitoTracker Deep Red results after 130hours of DPI treatment, which indicates an increase in mitochondrialmass. The bar graph shows that the mitochondrial mass (MitoTracker) isincreased after treatment with DPI, over the same time-frame. Theincrease in M-H cell propagation and mitochondrial mass with increasingconcentration of DPI treatment is consistent with the high basalglycolytic rate for M-H cells, discussed above.

The S-H cells, on the other hand, are sensitive to DPI, as can be seenin FIGS. 8A-C. FIG. 8A is a bar graph showing mammosphere formation inMCF7 cells at various concentrations of DPI, and including Ribocielib at100 nM. FIG. 8B shows the change in S-H cells decreasing from control to100 nM of DPI and Ribociclib. The bar graph shows that mammosphereformation in MCF7 cells is inhibited in response to DPI or Ribociclibtreatment, in a dose-dependent manner (0.50 and 100 nM). FIG. 8C shows aseries of flow cytometry tracings for S-H cells after treatment with 100nM DPI (showing a reduction in S-H cells). As can be seen. DPI hasopposite effects on M-H and S-H cells, namely, DPI selectively targetsthe S-H sub-population of e-CSCs. This demonstrates that e-CSCs aremetabolically-wired differently, depending on whether the cell isproliferating in a 2D-monolayer or a 3D-spheroid micro-environment. Mostimportantly, e-CSCs derived from 3D-spheroids are highly oxidative andcan be effectively targeted with an OXPHOS inhibitor. Non-limitingexamples of other OXPHOS inhibitors that may be used in the presentapproach include atovaquone, irinotecan, sorafenib, niclosamide, andberberine chloride.

Complementary experiments were carried out with Ribociclib, aclinically-approved CDK4/6 inhibitor. Ribociclib is normally used totreat female breast cancer patients, in combination with letrozole (anaromatase inhibitor). Ribociclib was first developed by AstexPharmaceuticals (Cambridge, UK) and Novartis. In 2017, Ribociclib wasapproved by the FDA and the European Medicines Agency, for the treatmentof HR-positive, HER2-negative advanced or metastatic breast cancers. Thedrug's most common side-effects are: neutropenia, anemia andGI-distress. The data for treatment with Ribociclib in FIGS. 8A-Cillustrate that treatment with Ribociclib effectively inhibits thepropagation of S-H cells. Therefore, anchorage-independent proliferationby S-H cells is critically-dependent on CDK4/6 function, as well asmitochondrial metabolism. The Ribociclib structure is shown below:

It should be appreciated that Ribocielib is one example of a CDK4/6inhibitor, and that other CDK4/6 inhibitors may be used under thepresent approach. Other non-limiting examples of CDK4/6 inhibitorsinclude, but are not limited to, abemacielib (Verzenio) and palbociclib(Ibrance).

Mitochondrial inhibitors may be used to eradicate e-CSCs. In the presentapproach, the mitochondrial inhibitors may be or include one or more of:a mitoribosein, the combination of an oxidative metabolism inhibitor anda glycolytic metabolism inhibitor, a repurposein, an antimitosein, amitoketosein, a mitoflavosein, a mitoflavin, a TPP-derivative, anMDIVI-1 derivative, chloramphenicol, puromycin and other inhibitors ofprotein synthesis (including. e.g., aminoglycosides and rapamycinanalogues), anti-parasitic drugs (such as, e.g., pyrvinium pamoate, andniclosamide), chloroquine, stiripentol, caffeic acid phenyl ester(CAPE), Vitamin C, 2-Deoxy-Glucose (2-DG), MCT1 inhibitors (AZD3965 andAR-C155858), D-Glucosamine, quercetin, and carvedilol. It should beappreciated that a therapeutic compound may fall under more than onecategory. The following paragraphs describe certain categories ofmitochondrial biogenesis inhibitor therapeutics. For brevity, therelated co-pending applications are incorporated by reference as iffully set forth herein.

A first category of therapeutics are mitoriboseins, as described inInternational Application No. PCT/US2018/022403, filed Mar. 14, 2018,and incorporated by reference in its entirety. The incorporatedreference includes data for select mitoribosein compounds. Generally,mitoriboseins are mitochondrial inhibitor compounds that haveanti-cancer and often antimicrobial activity, chemotherapy-sensitizing,radiosensitizing, and photosensitizing effects, as well as anti-agingeffects. These compounds hind to either the large sub-unit or the smallsub-unit of the mitoribosome (or in some instances, both) and inhibitmitochondrial biogenesis. Examples of mitoribosein groups, along withgeneric chemical structures and specific compounds, are described in theincorporated application, and include mitoribocyclines, mitoribomyeins,mitoribosporins, and mitoribofloxins.

A second category of mitochondrial biogenesis inhibitor therapeuticsinclude combination therapies involving oxidative metabolism inhibitorsand glycolytic metabolism inhibitors, e-CSCs in a mass may be reducedtargeted and eradicated by administering a pharmaceutically effectiveamount of at least one oxidative metabolism inhibitor, and at least oneglycolytic metabolism inhibitor. Inhibitors of oxidative metabolism mayinclude members of the tetracycline family and the erythromycin family.Members of the tetracycline family include tetracycline, doxycycline,tigecycline, minocycline, chlortetracycline, oxytetracycline,demeclocycline, lymecycline, meclocycline, methacycline,rolitetracycline, chlortetracycline, omadacycline, and sarecycline.Members of the erythromycin family include erythromycin, azithromycin,and clarithromycin. Glycolytic metabolism inhibitors may be selectedfrom inhibitors of glycolysis, inhibitors of OXPHOS, and inhibitors ofautophagy. Examples of glycolysis inhibitors include 2-deoxy-glucose,ascorbic acid, and stiripentol, OXPHOS inhibitors include atoravaquone,irinotecan, sorafenib, niclosamide, and berberine chloride. Autophagyinhibitors include chloroquine. Data and further examples are describedin International Application No. PCT/US2018/28587, filed Apr. 20, 2018,which is incorporated by reference in its entirety.

Some embodiments of combination therapies may take the form of a triplecombination. For example, in some embodiments of the present approach, afirst antibiotic inhibiting the large mitochondrial ribosome (such as,for example, members of the erythromycin family), and a secondantibiotic inhibiting the small mitochondrial ribosome (such as, forexample, members of the tetracycline family), may be administered with apro-oxidant or an agent inducing mitochondrial oxidative stress % (e.g.,low concentrations of Vitamin C, radiation therapy, among otherexamples). As a specific example, FDA-approved antibiotics doxycyclineand azithromycin may be used in connection with one or more commondietary supplements (e.g., Vitamin C). In an example embodiment,treatment with a combination of doxycycline (at 1 μM), azithromycin (at1 μM), and Vitamin C (at 250 μM) may be used as the mitochondrialbiogenesis inhibitor. The pro-oxidant may be, in some embodiments, atherapeutic agent having a pro-oxidant effect. For example, thepro-oxidant may be a therapeutic agent at a concentration that causesthe therapeutic agent to act as a reducing agent. U.S. ProvisionalPatent Application 62/780,488, filed Dec. 17, 2018 and incorporated byreference in its entirety, provides further description of triplecombination therapies.

Antimitoseins are a third category of mitochondrial biogenesisinhibitors, described more fully in International Patent ApplicationPCT/US2018/033466, filed May 18, 2018 and incorporated by reference inits entirety. Existing antibiotics having intrinsic anti-mitochondrialproperties can be chemically modified to target the mitochondria andinhibit mitochondrial biogenesis. The term “antimitosein” broadly refersto an antibiotic having intrinsic anti-mitochondrial properties that ischemically modified to target the antibiotic to mitochondria.Previously, intrinsic anti-mitochondrial activity in antibiotics wasconsidered to be an unwanted side-effect. Indeed, some potentialantibiotics have been excluded from trials due to excessiveanti-mitochondrial properties, and researchers have viewedanti-mitochondrial activity as a potential drawback. However, under thepresent approach, an antibiotic's intrinsic anti-mitochondrial activitycan become the basic for an entirely new therapeutic. The antimitoseinmay be an antibiotic having intrinsic anti-mitochondrial propertieschemically modified with a mitochondrial targeting signal (e.g., achemical moiety). Chemical modification may be, for example, throughcovalent or non-covalent bonds. In some embodiments, the antibiotic isone of a member of the tetracycline family, the erthyromyein family,chloramphenicol, pyrvinium pamoate, atovaquone, and bedaquiline. Themitochondria-targeting signal may be at least one compound or moietyselected from the group comprising a membrane targeting signal and amitochondrial ribosome-targeting signal. Examples of membrane targetingsignals include short-chain (e.g., fewer than 6 carbon atoms in thechain) fatty acids and medium-chain (e.g., 6-12 carbon atoms in thechain) fatty acids, palmitic acid, stearic acid, myristic acid, andoleic acid. Examples of mitochondrial ribosome-targeting signals includetri-phenyl-phosphonium (TPP) and guanidinium-based moieties. TPP andguanidinium are non-toxic chemical moieties that functionally behave asa mitochondrial targeting signal (MTS) in living cells. Either may bebonded to an antibiotic, often through the use of a carbon spacer-arm orlinking chain.

A fourth category of mitochondrial biogenesis inhibitors aremitoketoseins, non-carcinogenic compounds that bind to at least one ofACAT1/2 and OXCT1/2 and inhibit mitochondrial ATP production. Thesecompounds are described more fully in International ApplicationPCT/US2018/039354, filed Jun. 25, 2018, and incorporated by reference inits entirety. Generally, a mitoketosein targets the mitochondrialenzymes responsible for ketone re-utilization and that have anti-cancerand antibiotic properties. These compounds bind to either or both activecatalytic sites of OXCT1/2 and ACAT1/2 to inhibit mitochondrialfunction.

Mitoflavoseins and mitoflavins are a fifth category of mitochondrialbiogenesis inhibitors that may be used under the present approach. Thesecompounds are described more fully in International Patent ApplicationPCT/US2018/057093, filed Oct. 23, 2018 and incorporated by reference inits entirety. Mitoflavoseins are compounds that bind toflavin-containing enzymes and inhibit mitochondrial ATP production.Diphenyleneiodonium chloride (DPI) is an example of a mitoflavosein. Itshould be appreciated that a mitoflavosein may be modified with amitochondrial targeting signal, such as discussed above with respect toantimitoseins. Mitoflavins, derivatives of riboflavin that inhibitmitochondrial function, may also be chemically modified with amitochondrial targeting signal. For example, roseoflavin[8-Demethyl-8-(dimethylamino)-riboflavin or 8-Dimethylaminoriboflavin]is a naturally occurring anti-bacterial compound that is a derivative ofriboflavin, which can be chemically modified to optimize its potentialfor targeting CSCs and inhibiting mitochondrial biogenesis. Lumichrome(7,8-Dimethylalloxazine) is a fluorescent photoproduct of riboflavindegradation, which also can be chemically modified to optimize itspotential for targeting CSCs. Other common derivatives of riboflavininclude: Alloxazine, Lumiflavine, 1,5-dihydroriboflavin and1,5-dihydroflavin. Each of these riboflavin derivatives may bechemically modified with a mitochondrial targeting signal to form amitoflavin, and may be used as a mitochondrial biogenesis inhibitoraccording to the present approach.

A sixth category of mitochondrial biogenesis inhibitors isTPP-derivative compounds that show not only a strong preference foruptake in cancer cells (bulk cancer cells, cancer stem cells, andenergetic cancer stem cells), but also disrupt mitochondrial biogenesisin these cells. These TPP-derivative compounds are described more fullyin International Patent Application PCT/US2018/062174, filed Nov. 21,2018 and incorporated by reference in its entirety. As used with respectto TPP-derivatives, a derivative as known in the art is a compound thatcan be synthesized from a parent compound by replacing an atom withanother atom or group of atoms. For example, a derivative of TPP is2-butene-1,4-bis-TPP, which includes two phosphonium groups joined bybutene. A derivative of 2-butene-1,4-bis-TPP, then, could includereplacement of one or more phenyl groups with another compound, such asa halogen or an organic compound. For the sake of brevity, thisdisclosure does not identify all of the potential derivatives, as thedescription should be adequate for a person of ordinary skill in theart. Other examples of TPP-derivative compounds that may be used asmitochondrial biogenesis inhibitors according to the present approachinclude 2-butene-1,4-bis-TPP; derivatives of 2-butene-1,4-bis-TPP;2-chlorobenzyl-TPP; derivatives of 2-chlorobenzyl-TPP;3-methylbenzyl-TPP; derivatives of 3-methylbenzyl-TPP;2,4-dichlorobenzyl-TPP; derivatives of 2,4-dichlorobenzyl-TPP;1-naphthylmethyl-TPP; derivatives of 1-naphthylmethyl-TPP;p-xylylenebis-TPP; and derivatives of p-xylylenebis-TPP. Of course, itshould be appreciated that the foregoing list is not an exhaustive listof TPP-derivatives.

Repurposeins are a seventh category of mitochondrial biogenesisinhibitors that may be used in embodiments of the present approach.International Patent Application PCT/US2018/062956, filed Nov. 29, 2018and incorporated by reference in its entirety, describes these compoundsmore fully. Generally, “repurposeins” are compounds having intrinsicanti-mitochondrial properties that are chemically modified to target thecompounds to mitochondria. Such compounds may include, for example,FDA-approved pharmaceuticals, nutraceuticals, and supplements, amongothers. Compounds having intrinsic anti-mitochondrial properties may bechemically modified with one or more mitochondrial targeting signals asdescribed above. Examples of compounds having intrinsicanti-mitochondrial properties include berberine chloride, quercetin,niclosamide, acriflavinium hydrochloride, sorafenib, emetinedihydrochloride, dactinomycin, plicamycin, suloctidil, teniposide,pentamidine isethionate, daunorubicin, thioguanine, amsacrine,phenformin hydrochloride, irinotecan hydrochloride, mitomycin,hydroxyprogesterone caproate, cyclosporine, lanatoside c,mercaptopurine, quinacrine hydrochloride, and fenofibrate. In someembodiments, the compound may be one or more of neomycin, puromycin,rapamycin (and its derivatives, such as everolimus), G418,trovafloxacin, levonoxacin, avocatin B, clarithomycin, ciprofloxacin,spiramycin, telithromycin, norfloxacin, moxifloxacin, ofloxacin,minocycline, tetracycline, demethylchlortetracycline, a member of thetetracycline family, a member the erythromycin family, clindamycin,metronidazole, linezolid, mupirocin, vancomycin, clindamycin,cephalosporin, ciprofloxacin, streptomycin, amoxicillin, and azelaicacid. It should be noted that a repurposein formed from an antibioticmay also be referred to as an antimitosein.

An eighth category of mitochondrial biogenesis inhibitors that may beused in the present approach is MDIVI-1 derivatives, as described inInternational Patent Application PCT/US2018/066247, filed Dec. 18, 2018and incorporated by reference in its entirety. Mitochondrial divisioninhibitor-1 (mDIVI-1) is a small molecule that selectively andreversibly inhibits DRP1. MDIVI-1 has been shown to target DRP1 bybinding and suppressing both the DRP1 self-assembly into ring-likestructures around the mitochondria and its capacity to catalyze GTPhydrolysis. The present approach may take the form of a mitochondrialfission inhibitor 1 (mDIVI-1) derivative having the general formula:

or a pharmaceutically acceptable salt thereof, wherein each of R1through R8 may be selected from the group consisting of hydrogen,carbon, nitrogen, sulfur, oxygen, fluorine, chlorine, bromine, iodine,carboxyl, alkanes, cyclic alkanes, alkane-based derivatives, alkenes,cyclic alkenes, alkene-based derivatives, alkynes, alkyne-basedderivatives, ketones, ketone-based derivatives, aldehydes,aldehyde-based derivatives, carboxylic acids, carboxylic acid-basedderivatives, ethers, ether-bused derivatives, esters and ester-basedderivatives, amines, amino-based derivatives, amides, amide-basedderivatives, monocyclic or polycyclic arenes, heteroarenes, arene-basedderivatives, heteroarene-based derivatives, phenols, phenol-basedderivatives, benzoic acid, benzoic acid-based derivatives, and amitochondrial targeting signal. In some embodiments, at least oneR-group is a mitochondrial targeting signal, such as palmitic acid,stearic acid, myristic acid, and oleic acid, a short-chain fatty acid, amedium-chain fatty acid, tri-phenyl-phosphonium (TPP), a TPP-derivative,a lipophilic cation, and 10-N-nonyl acridine orange. In someembodiments, at least one R-group is a mitochondrial targeting signal,such as one of 2-butene-1,4-bis-TPP; 2-chlorobenzyl-TPP;3-methylbenzyl-TPP; 2,4-dichlorobenzyl-TPP; I-naphthylmethyl-TPP;p-xylylenebis-TPP; a derivative of 2-butene-1,4-bis-TPP; a derivative of2-chlorobenzyl-TPP; a derivative of 3-methylbenzyl-TPP; a derivative of2,4-dichlorobenzyl-TPP; a derivative of 1-naphthylmethyl-TPP; and aderivative of p-xylylenebis-TPP. It should be appreciated that MDIVI-1derivatives may be used us mitochondrial inhibitors under the presentapproach, with one or more of the chemical modifications described inthis paragraph.

Although the data described above relates to breast cancer cells, itshould be appreciated that the e-CSC phenotype is not limited to MCF7cells. Data for MDA-MB-468 cell subpopulations shows that e-CSCs havecommon characteristics across different cancer types. FIGS. 9A-9D showcell cycle progression data for MDA-MB-468 e-CSCs. The e-CSCs wereseparated as described above, with respect to MCF7 cells. Representativeimages of the cell cycle analysis for M-L and M-H sub-populations ofMDA-MB-468 cells grown in a monolayer arm shown in FIG. 9A. The cellcycle progression for M-L, M-H, S-L, and S-H sub-populations issummarized the bar graphs in FIGS. 9B and 9C. Mitotracker Deep Red datais presented in FIG. 9D. The S-H cell sub-population derived fromMDA-MB-468 cells shows the largest increases in cell cycle progressionand mitochondrial mass. Virtually identical results were obtained withMCF7 cells, as discussed above.

FIGS. 10A-10D show OCR data for MDA-MB-468 cell sub-populations. Datafor the M-L and M-H sub-populations are in FIGS. 10A and 10B, and datafor the S-L and S-H sub-populations are in FIGS. 10C and 10D. As withthe MCF7 data discussed above, the oxygen consumption rate (OCR) forthese sub-populations was measured using the Seahorse XFe96metabolic-flux analyzer. The high OCR in MDA-MB-468 cells directlycorrelates with high-flavin content. For example, M-H cells (from2D-monolayers) and S-H cells (from 3D-spheroids) have the highest levelsof OCR, as compared to the M-L and S-L sub-populations.

FIGS. 11A-11D show ECAR data for same MCF7 cell sub-populations measuredusing the Seahorse XFe96 metabolic-flux analyzer. FIGS. 11A and 11B showECAR data for M-L vs. M-H sub-populations. FIGS. 11C and 11D show ECARdata for S-L vs. S-H sub-populations. The high ECAR in MDA-MD-468 cellsdirectly correlates with high-flavin content. For example, M-H cells(from 2D-monolayers) and S-H cells (from 3D-spheroids) have the highestlevels of ECAR, as compared to the M-L and S-L sub-populations.

The following paragraphs describe the proteomics analysis of e-CSCsderived from MCF7 3D-spheroid cells. Label-free unbiased proteomicsanalysis was used to describe the mechanistic basis for the biogenesisof e-CSCs. As a consequence, 225 proteins were identified that weresignificantly up-regulated by ≥1.5-fold. Conversely, 187 proteins weresignificantly down-regulated. For simplicity, the analysis focused onthe specific protein products that were up-regulated and these are shownin Table 4, below. Interestingly, 48 of these proteins (representing˜20% of the total number) were specifically related to mitochondrialenergy production and/or mitochondrial biogenesis. These 48 proteins areidentified in Table 5, below. This is consistent with the functionalobservations described above, that e-CSCs demonstrate a near 4-foldincrease in both mitochondrial mass and mitochondrial ATP production.

Table 6 shows further bioinformatics analysis, assembling the proteinsinto distinct functional groups. These functional classes includesenescence, the anti-oxidant response, “stemness,” cytoskeletal proteins(suggestive of an EMT), glutamine metabolism, NADH/NADPH synthesis,flavin-containing enzymes, autophagy/lysosomes, peroxisomes, and variouscellular markers (epithelial, cell surface, S100 family proteins, RABs,annexins, PARP, calcium signaling). Interestingly, CDKN1A (p21 WAF),which is a CDK-inhibitor and senescence marker, is highly up-regulatedby 17.22-fold in e-CSCs. This finding is consistent with the idea thatCSCs originate from senescent cells.

However, e-CSCs are hyper-proliferative, so they likely escaped fromsenescence. This may have occurred through the over-expression ofanti-oxidant enzymes or the over-production of NADH/NADPH. Loss ofglutaredoxin expression is known to be sufficient to induce a senescencephenotype in cells, in a p21-dependent manner. Therefore, the observedover-expression of glutaredoxin (by 10.79-fold) may be sufficient toactually overcome senescence, allowing the creation of e-CSCs.

Importantly, glutaredoxin expression is known to drive mitochondrialbiogenesis by directly regulating the activation state of two keymitochondrial proteins, namely HSP60 and DJ-1 (Park7) (35). HSP60 is amitochondrial chaperone, which facilitates the proper folding of newlysynthesized or imported mitochondrial proteins, while DJ-1 functionallymaintains the activity of mitochondrial complex 1 and SOD2. As aconsequence, glutaredoxin expression specifically maintains theintegrity of mitochondria and elevates ATP synthesis. Glutaredoxin'sability to regulate mitochondrial energy production is also linked tocell cycle progression. As such, glutaredoxin allows cells tosuccessfully pass through the GUS transition, in a CDK4-dependentmanner, thereby avoiding the cell-cycle arrest associated withsenescence.

The 10.24-fold up-regulation of ALDH3A1 also provides significantanti-oxidant power, as ALDH isoforms are known to functionally increasethe cellular levels of NADH/NADPH. Also, the main isoform up-regulatedin e-CSCs, namely ALDH3A1, is known to be associated with tumorigenesis,metastasis and drug-resistance. Ingenuity Pathway Analysis (IPA) of theproteomics data sets referenced above confirmed the up-regulation of theanti-oxidant response and cell cycle progression in e-CSCs, as well asthe changes in mitochondrial function IPA analysis of the proteomicsdata sets showed the activation of upstream regulator PPARGC-1-Alpha1301, also known as PGC-1-Alpha, the major mitochondrial transcriptionfactor. Also, a canonical pathway assessment identified theNRF2-mediated oxidative stress response pathway, and the cell cycle G2/MDNA damage checkpoint regulation pathway as significantly up-regulated.

In vivo data demonstrates that e-CSC proteins are transcriptionallyup-regulated in human breast cancer patients. Pre-existing mRNAprofiling data, obtained from the laser-capture analysis of N=28 humanbreast cancer patient tumor samples were used to determine if e-CSCsproteins were also transcriptionally up-regulated in human breast cancercells in vivo. In this data set, breast cancer cells were physicallyseparated from adjacent tumor stromal cells, using laser-capturedmediated micro-dissection. The results of this intersection arepresented in Table 7. The results indicate that out of the 225 proteinsthat were up-regulated in e-CSCs, nearly one-third of these geneproducts were transcriptionally up-regulated in human breast cancercells (70/225=31.11%). In addition, many of these gene products wereshared, including 20 mitochondrial related genes (20/70=28.57%). Theseresults provide genetic evidence that demonstrates the clinicalrelevance of e-CSCs in the study of human breast cancers.

In connection with the prior work, a short anti-oxidant responsesignature in e-CSCs has been developed that predicts poor clinicaloutcome in breast cancer patients. The next paragraphs describeidentifying subsets of e-CSCs proteins that have prognostic value interms of predicting clinical outcome in human breast cancer patients. Awell-defined set of high-risk ER(+) patients (luminal A) that receivedhormone-therapy (mostly-tamoxifen), with local Lymph-Node (LN)metastasis at diagnosis, as well as >150 months (12.5 years) offollow-up data were used for this assessment. In all of these cancerpatients, their breast tumor tissues also underwent genomictranscriptional profiling.

Kaplan-Meier (K-M) analysis was used to specifically determine whetherthese e-CSCs proteins listed in Table 7 had prognostic value bydetermining their effects on the Hazard-Ratio (HR), by employing theLog-Rank test to determine statistical significance. Based on thisanalysis, a four-gene signature consisting of members of theanti-oxidant response and NAD(P)H metabolism, namely NQO1, ALDH5A1, TXNRand RRM2, was developed. Table 8, below, summarizes the results. Theother 66 gene products tested did not show this prognostic ability.

The K-M curves shown in FIGS. 12A-12I show that the anti-oxidantsignature from e-CSCs of the present approach, NQO1, ALDH5A1, TXNR, andRRM2, effectively predicts tumor recurrence in all of the breast cancersub-types tested. The patient groups examined were as follows: FIG. 12Ashows ER(+), Luminal A sub-type, with Lymph-Node metastasis (LN(+)) atdiagnosis, and treated with hormonal therapy (TAM/HT) (N=150). FIG. 12Bshows ER(+). Luminal A sub-type, and treated with hormonal therapy(TAM/HT) (N=538). FIG. 12C shows ER(+) and treated with hormonal therapy(TAM/HT) (N=804). FIG. 12D shows all ER(+) (N=2.780). FIG. 12E shows allER(−) (N=791). FIG. 12F shows all Breast Cancer (N=3.571). FIG. 12Gshows ER(+), with the Luminal B sub-type (N=266). FIG. 12H shows ER(−),with the Basal sub-type (N=561). Finally, FIG. 12I shows ER(−) andHER2(+) (N=230).

These four gene products (NQO1, ALDHSA1, TXNR, and RRM2) were testedindividually and all showed a >2-fold increase in the HR. In addition,when combined into a short signature, this resulted in a HR of nearly 4,with a p-value of 4.1e-05. As a consequence, under the present approachthis anti-oxidant signature may be used to predict tumor recurrence(FRS) in patients receiving hormonal therapy.

Similarly, the inventors also recognized that the transcriptionalelevation of 3 out of 4 of these gene products (ALDH5A1, TXNR and RRM2)was effectively able to predict distant metastasis (DMFS), with HRs of2.86 to 3.64, and p-values of 0.003 to 0.00035. Table 9, below, showsthese results. Thus, under the present approach a three-gene signatureof ALDH5A1. TXNR and RRM2 may be used to identify a risk of distantmetastasis.

It should be appreciated that the anti-oxidant response in e-CSCsallowed the inventors to successfully identify gene products withpredictive value, for anticipating the onset of recurrence and/ormetastasis, in breast cancer patients that ultimately underwenttreatment failure, in response to hormonal therapy. K-M curves forlarger groups of breast cancer patients are also shown FIGS. 12A-12I,which all showed significant prognostic value. These included patientsthat were ER(+) (N=2.780), shown in FIG. 12D, and ER(−) (N=791), shownin FIG. 12E, as well as all breast cancer sub-types, taken together(N=3.571), FIG. 12F. Therefore, this signature should have broadapplicability in breast cancer, and other cancer types.

Embodiments of the present approach may involve measuring or determiningthe expression level of one or more genes in the e-CSC gene signature(NQO1, ALH5A1, TXNR, and RRM2). The e-CSC gene signature has prognosticvalue with respect to the presence of e-CSCs, tumor recurrence due tatleast in part) to e-CSCs, and distant metastasis due (at least in part)to e-CSCs. Up-regulation of one or more of the genes in the e-CSC genesignature may be used as a biomarker indicating that treatment fore-CSCs may be beneficial. Treatment for e-CSCs includes administering apharmaceutically effective amount of at least one of an OXPHOS inhibitorand a CDK4/6 inhibitor. Additionally, e-CSCs may be treated with atleast one mitochondrial biogenesis inhibitor. The treatment for e-CSCsmay reduce or eliminate the likelihood of distant metastasis and/ortumor recurrence, and also may improve the effectiveness of other cancertherapies. It should be appreciated that gene expression levels may bemeasured using assays known to those having ordinary skill in the art.Gene expression may be measured based on the protein gene product, andcommon techniques include expression proteomics. Western blotting, andenzyme-linked immunosorbent assay (sometimes referred to as the ELISAassay). Gene expression may also be measured based on mRNA levels, andcommon techniques for mRNA level measurement include Northern blottingand reverse transcription then quantitative polymerase chain reaction(also called RT-qPCR). The threshold or baseline level(s) may beobtained from available literature and/or databases known in the art.Also, the threshold or baseline level(s) may be obtained from using anassay on a biologic sample representing a normal, healthy cell line. Asthose having at least an ordinary level of skill in the art willappreciate, the threshold or baseline level(s) may also be determinedfrom in vivo data of patients having the same cancer, but no symptoms oftumor recurrence and/or distant metastasis. For example, in someembodiments the threshold data may be derived from the K-M-plottersource referenced above. In such embodiments, overexpression of one ormore genes from the e-CSC gene signature in a mass, relative to thethreshold data, indicates the presence of e-CSCs, and is prognostic of alikelihood of tumor recurrence and/or metastasis. The overexpression maybe quantified as a ratio, and the ratio for determining overexpressiondepends on the embodiment. For example, in some embodimentsoverexpression may be determined if the quotient of the determined leveldivided by the threshold level is greater than 1.2. As further examples,in some embodiments the ratio may be 1.4, and in some embodiments is maybe 1.6. It should be appreciated that the present approach is notlimited to a particular threshold or metric for indicatingoverexpression 13 the person of ordinary skill in the art may identify athreshold data source for a particular cancer, and select the ratio fordiagnosing overexpression of one or more genes of the e-CSC genesignature. This gene signature has prognostic value, as discussed above,and may be used as a biomarker of e-CSCs and the risk of tumorrecurrence and/or metastasis due (at least in part) to e-CSCs. In turn,the e-CSC gene signature may be used to identify instances in which apharmaceutically effective amount of at least one of an OXPHOSinhibitor, a CDK4/6 inhibitor, and/or a mitochondrial biogenesisinhibitor, may be administered to target e-CSCs in a mass, treat (i.e.,reduce the likelihood of) distant metastasis and/or tumor recurrence,and treat cancer.

One explanation for the prognostic value of this compact gene signatureis that an adaptive anti-oxidant response drives resistance to bothchemotherapy and radiotherapy in cancer patients. In addition, TXNR andRRM2 both are key enzymes that provide the required precursors fornucleotide biosynthesis and, hence, cell cycle progression. Theanti-oxidant response signatures of the present approach may also beuseful for identifying breast cancer patients that could benefit fromtreatment with i) mitochondrial inhibitors or ii) CDK inhibitors,especially in the context of preventing tumor recurrence and/or distantmetastasis. In some embodiments, these treatments may be usedindividually, or in combination with other therapies, such as (but notlimited to) chemotherapy and radiation therapy. Thus, in the future, ROSproduction in e-CSCs, under both 2D and 3D microenvironmentalconditions, will be used to validate that ROS production is driving thisanti-oxidant response signature and contributes to their overallenergetic phenotype.

The following paragraphs describe the methodologies and materials usedin connection with the foregoing. It should be appreciated by those ofordinary skill in the art that variations may be made without departingfrom the present approach. With respect to the breast cancer cell modelsand other reagents, human breast cancer cell lines, MCF7 (ER(+)) andMDA-MB-468 (triple-negative), were obtained commercially from the ATCC.Both cell lines were maintained in Dulbecco's Modified Eagle Medium(DMEM: GIBCO), supplemented with 10% FBS, 1% Glutamax and 1%Penicillin-Streptomycin. All cell lines were maintained at 37° C. in 5%CO2. DPI and Ribociclib were purchased from Sigma-Aldrich, Inc.

Cell sorting: Flow-cytometry and collection of auto-fluorescent cells:MCF7 and MDA-MB-468 cells were first grown either as a 2D-monolayer oras 3D-spheroids. Then, they were collected and dissociated into asingle-cell suspension, prior to analysis or sorting by flow-cytometrywith the SONY SH800 Cell Sorter. Briefly, auto-fluorescent cells wereexcited with a 488 nm blue laser and selected at the intersection withfilters 525/50 and 585/30. The “Low” and “High” auto-fluorescent cellsub-populations were selected by gating, within the auto-fluorescencesignal. Only cells with the least (bottom 5%) or the most (top 5%)auto-fluorescence signal were collected. The cells outside the gateswere discarded during sorting, due to the gate settings. However, suchsettings am required, to ensure high-purity during sorting. To bettercharacterize the auto-fluorescent cell sub-populations, the followingflow-cytometry markers were used: ALDEFLUOR-assay (StemCelltechnologies, Durham, N.C., USA); and MitoTracker Deep Red (ThermoFisher Scientific). Hoescht (Thermo Fisher Scientific) was used for cellcycle analysis. Data were analyzed with FlowJo 10.1 software.

Preparing cells for auto-fluorescent cell sorting by flow-cytometry: Thefollowing protocol was used to acquire and sort auto-fluorescent cellsfrom 2D-monolayers or 3D-spheroid cell suspensions. For 2D-monolayers.MCF7 and MDA-MB-468 were seeded in a 225 cm2 flask and when ˜70%confluence was reached, 5 ml of 0.025% trypsin was added to the flasksand incubated at 37° C. for 5 minutes. After that the cells werere-suspended in media and centrifuged at 300 g for 5 min. Aftercentrifugation, the cell pellets were adjusted to a concentration of 106cells/ml in in PBS Ca/Mg for acquisition or in sorting buffer (1×PBScontaining 3% (v/v) FBS and 2 mM EDTA) for FACS.

For 3D-spheroid suspensions, after 5 days of growth under low-attachmentcondition, the spheres were collected from six 225 cm2 flasks pre-coatedwith poly-HEMA and gently centrifuged at 100 g for 5 min. Aftercentrifugation, 1 ml of 0.025% of trypsin was added to the“sphere-pellet” and incubated them at 37° C. for 5 minutes. Using a25-gauge needle, the sphere-suspension was passed through the syringe 4times. The sphere suspension was then centrifuged again at 100 g for 5min, and the sphere-pellet was re-suspended in i) PBS Ca/Mg foracquisition or ii) in sorting buffer (1×PBS containing 3% (v/v) FBS and2 mM EDTA) for FACS and the suspension was “syringed” again 4 times.After creating these single-cell suspensions, they were subjected tostandard flow-cytometry (using the SONY SH800 Cell Sorter) to isolatethe auto-fluorescent cell sub-populations, as indicated above. Examplesof flow-cytometry plots are included in the figures, and the gatingstrategy is shown.

Mammosphere formation assay (for generating 3D-spheroids): A single-cellsuspension was prepared using enzymatic, and manual disaggregation (25gauge needle). Then, cells were plated at a density of 500 cells/cm2 inmammosphere medium (DMEM-F12+B27+20 ng/ml EGF+PenStrep) undernon-adherent conditions, in culture dishes pre-coated with(2-hydroxyethylmethacrylate) (poly-HEMA, Sigma, #P3932), called“mammosphere plates.” Cells were grown for 5 days and maintained in ahumidified incubator at 37° C. After 5 days of culture, 3D-spheres>50 μmwere counted using an eye piece (“graticule”), and the percentage ofcells plated which formed spheres was calculated and is referred to aspercent mammosphere formation, and was normalized to one (1=100% MSF).Mammosphere formation efficiency was analyzed in both the “low” and“high” sub-populations of auto-fluorescent cells, generated from either2D-monolayers (M-L vs. M-H) or 3D-spheroids (S-L vs. S-H). Allmammosphere experiments were performed in triplicate, at least 3 timesindependently.

ALDEFLUOR assay: The level of ALDH activity was assessed, by using thefluorescent reagent ALDEFLUOR. The ALDEFLUOR kit (StemCell technologies,Durham, N.C., USA) was used to detect the cell sub-populations withvarious amounts of ALDH enzymatic activity by FACS (Attune N×T FlowCytometer). Briefly, 1×105 were incubated in 1 ml ALDEFLUOR assay buffercontaining ALDH substrate (5 μl/ml) for 40 minutes at 37° C. In eachexperiment a sample of cells was stained under identical conditions with30 μM of diethylaminobenzaldehyde (DEAB), a specific ALDH inhibitor, asa negative control The ALDH-positive population was established,according to the manufacturer's instructions and was evaluated using50,000 cells. All the ALDH experiments were performed three timesindependently.

Seahorse XFe96 metabolic flux analysis: Real-time oxygen consumptionrates (OCR) and extracellular acidification rates (ECAR) rates weredetermined using the Seahorse Extracellular Flux (XFe96) analyzer(Seahorse Bioscience. USA). Briefly, 2×104 cells per well were seededinto XFe96 well cell culture plates after sorting, and incubated for 12h to allow cell attachment. After 12 hours of incubation, cells werewashed in pre-warmed XF assay media (or for OCR measurement. XF assaymedia supplemented with 10 mM glucose, 1 mM Pyruvate, 2 mM L-glutamineand adjusted at 7.4 pH). Cells were then maintained in 175 μL/well of XFassay media at 37° C. in a non-CO2 incubator for 1 hour. During theincubation time, 25 μL of 80 mM glucose, 9 μM oligomycin, and 1M2-deoxyglucose (for ECAR measurement) or 10 μM oligomycin, 9 μM FCCP, 10μM rotenone, 10 μM antimycin A (for OCR measurement), was loaded in XFassay media into the injection ports in the XFe96 sensor cartridge (20,21). Measurements were normalized by protein content (SRB assay) andHoechst 33342 content. Data sets were analyzed using XFe96 software andGraphPad Prism software, using one-way ANOVA and Student's t-testcalculations. All experiments were performed in quintuplicate, threetimes independently.

Vital mitochondrial staining: Cells were trypsinized and re-suspendedinto a 1×106 cell/ml solution in PBS, 10 nM of MitoTracker Deep-Red(Thermo Fisher Scientific) was added for 30 minutes at 37° C. beforecentrifugation and re-suspension in PBS Ca/Mg for FACS analysis (ATTUNEN×T) or Cell Sorting (SONY SH 800). All subsequent steps were performedin the dark. Data analysis was performed using FlowJo software.

Cell cycle analysis: The cell-cycle analysis was performed on theauto-fluorescent cell sub-populations, by FACS analysis using the SONYCell Sorter. Briefly, after trypsinization, the re-suspended cells wereincubated with 10 ng/ml of Hoescht solution (Thermo Fisher Scientific)for 40 minutes at 37° C. under dark conditions. Following a 40 minuteperiod, the cells were washed and re-suspended in PBS Ca/Mg foracquisition or in sorting buffer (1×PBS containing 3% (v/v) FBS and 2 mMEDTA) for FACS. We analyzed 50,000 events per condition. Gated cellswere manually-categorized into cell-cycle stages.

Statistical analysis: All analyses were performed with GraphPad Prism 6.Data were represented as mean±SD (or ±SEM where indicated). Allexperiments were conducted at least 3 times independently, with >3technical replicates for each experimental condition tested (unlessstated otherwise, e.g., when representative data is shown).Statistically significant differences were determined using theStudent's t test or the analysis of variance (ANOVA) test. For thecomparison among multiple groups, one-way ANOVA were used to determinestatistical significance. P<0.05 was considered significant and allstatistical tests were two-sided. In the drawings, * indicatesP<0.05; **indicates P<0.005; and *** indicates P<0.0005.

Proteomics analysis: Label-free unbiased proteomics and Ingenuitypathway analysis (IPA) were carried out, essentially as previouslydescribed, using standard protocols, with relatively minormodifications. For IPA, unbiased interrogation and analysis of theproteomic data sets was carried out by employing the IPA bioinformaticsplatform (Ingenuity systems, http://www.ingenuity.com). IPA assists withdata interpretation, via the grouping of differentially expressed genesor proteins into known functions and pathways. Pathways with a z scoreof >+2 were considered as significantly activated, while pathways with az score of <−2 were considered as significantly inhibited.

Clinical relevance of e-CSC marker proteins: To validate the clinicalrelevance of our findings, the inventors first assessed whether theidentified e-CSC targets in MCF7 cells were also transcriptionallyupregulated in human breast cancer cells in vivo. For this purpose, theinventors employed a published clinical data set of N=28 breast cancerpatients in which their tumor samples were subjected to laser-capturemicro-dissection (26), to physically separate epithelial cancer cellsfrom their adjacent tumor stroma.

Kaplan-Meier (K-M) analyses: To perform K-M analysis on mRNAtranscripts, the inventors used an open-access online survival analysistool to interrogate publically available microarray data from up to3.455 breast cancer patients. This allowed us to determine theirprognostic value (27). For this purpose, the inventors primarilyanalyzed data from ER(+) patients that were LN(+) at diagnosis and wereof the luminal A sub-type, that were primarily treated with tamoxifenand not other chemotherapy (N=150 patients). In this group, 100% thepatients received some form of hormonal therapy and ˜95% of themreceived tamoxifen. Biased and outlier array data were excluded from theanalysis. This allowed us to identify metabolic gene transcripts, withsignificant prognostic value. Hazard-ratios were calculated, at the bestauto-selected cut-off, and p-values were calculated using the log-ranktest and plotted in R. K-M curves were also generated online using theK-M-plotter (as high-resolution TIFF files), using univariate analysis:http://kmplot.com/analysis/index.php?p=service&cancer=breast. Thisallowed us to directly perform in silico validation of these metabolicbiomarker candidates. The 2017 version of the database was utilized forall these analyses, while virtually identical results were also obtainedwith the 2014 and 2012 versions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the approach. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising.” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiments are therefore to be considered in all respects asillustrative and not restrictive, the scope of the invention beingindicated by the claims of the application rather than by the foregoingdescription, and all changes which come within the meaning and range ofequivalency of the claims are therefore intended to be embraced therein.

TABLE 4 Proteomic analysis of e-CSCs, derived from MCF7 3D-SpheroidsFold-Change Symbol Gene Description (Up-regulation) BCAS1 Breastcarcinoma-amplified sequence 1 119.37 CDKN1A Cyclin-dependent kinaseinhibitor 1 (p21-WAF/CDK-inhibitor) 17.22 GLRX Glutaredoxin-1 10.79ALDH3A1 Aldehyde dehydrogenase, dimeric NADP-preferring 10.24 CEACAM6Carcinoembryonic antigen-related cell adhesion molecule 6 9.66 CYP1A1Cytochrome P450 1A1 6.60 ELMOD2 ELMO domain-containing protein 2 4.73MAOA Amine oxidase [flavin-containing] A 4.73 KRT10 Keratin, type  

  cytoskeletal 10 4.59 IGFBP2 Insulin-like growth factor-binding protein2 4.20 QPRT Nicotinate-nucleotide pyrophosphorylase [carboxylating] 3.72MVP Major vault protein 3.61 CEACAM5 Carcinoembryonic antigen-relatedcell adhesion molecule 5 3.38 CLU Clusterin 3.13 QSOX1 Sulfhydryloxidase 1 2.93 CIB1 Calcium and integrin-binding protein 1 2.90 VGFNeurosecretory protein VGE 2.90 ANXA1 Annexin A1 2.87 AKR1C3 Aldo-ketoreductase family 1 member C3 2.79 LAMA5 Laminin subunit alpha-5 2.72CDC42BPG Serine/threonine-protein kinase MRCK gamma 2.69 RAB27BRas-related protein Rab-27B 2.69 CHMP6 Charged multivesicular bodyprotein 6 2.62 TUBA4A Tubulin alpha-4A chain 2.60 PARP4 Poly[ADP-ribose] polymerase 4 2.55 RAB27A Ras-related protein Rab-27A 2.54EVPL Envoplakin 2.48 KLK11 Kallikrein- 11 2.46 MAOB Amine oxidase[flavin-containing] B 2.45 DPP7 Dipeptidyl peptidase 2 2.43 AKR1C2Aldo-keto reductase family 1 member C2 2.41 SFXN3 Sideroflexin-3 2.40MIC13 MICOS complex subunit MIC13 

  mitochondrial 2.36 GM2A Ganglioside GM2 activator 2.36 SCRN2Secernin-2 2.34 SULT1A1 Sulfotransferasc 1A1 2.34 RRM2Ribonucleoside-diphosphate reductase subunit M2 2.34 SERPINA3Alpha-1-antichymotrypsin 2.33 SLC6A14 Sodium- and chloride-dependentneutral and basic amino acid transporter B(0+) 2.30 AGAN(4)-(beta-N-acetylglucosaminyl)-L-asparaginasc 2.30 SYTL2Synaptotagmin-like protein 2 2.30 MPV17 Protein Mpv17 2.28 KIAA0319LDyslexia-associated protein KIAA0319-like protein 2.25 B3GAT3Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase 3 2.22PON2 Serum paraoxonase/arylesterase 2 2.22 OXSM3-oxoacyl-[acyl-carrier-protein] synthase, mitochondrial 2.22 TOM1L2TOM1-like protein 2 2.22 STOM Erythrocyte band 7 integral membraneprotein 2.18 MROH1 Maestro heat-like repeat-containing protein familymember 1 2.17 PI4K2A Phosphatidylinositol 4-kinase type 2-alpha 2.17FECH Ferrochelatase, mitochondrial 2.16 MCU Calcium uniporter protein,mitochondrial 2.13 S100P Protein S100-P 2.11 RDH13 Retinol dehydrogenase13 2.08 PPL Periplakin 2.08 TSPAN31 Tetraspanin-31 2.03 TIMP1Metalloproteinase inhibitor 1 2.02 GCLC Glutamate-cysteine ligasecatalytic subunit 2.01 NEBL Nebulette 2.01 MUC5B Mucin-5B 1.98 CTSHCathepsin H 1.98 GNS N-acetylglucosamine-6-sulfatase 1.97 S100A10Protein S100-A10 1.96 INPP4B Type II inositol 3,4-bisphosphate4-phosphatase 1.96 PHYKPL 5-phosphohydroxy-L-lysine phospho-lyase 1.95ASAH1 Acid ceramidase 1.94 DHRS1 Dehydrogenase/reductase SDR familymember 1 1.93 PEX14 Peroxisomal membrane protein PEX14 1.91 PTGR1Prostaglandin reductase 1.91 NQO2 Ribosyldihydronicotinamidedehydrogenase [quinone] 1.90 STARD3NL STARD3 N-terminal-like protein1.88 MGST1 Microsomal glutathione S-transferase 1.88 CMC1 COX assemblymitochondrial protein homolog 1.87 DGAT1 DiacylglycerolO-acyltransferase 1 1.87 RAB24 Ras-related protein Rab-24 1.87 GDPD3Lysophospholipase D GDPD3 1.86 DCLK1 Serine/threonine-protein kinaseDCLK1 1.85 PSAP Prosaposin 1.85 MGST3 Microsomal glutathioneS-transferasc 3 1.84 ANO10 Anoctamin-10 1.84 CASK Peripheral plasmamembrane protein CASK 1.84 LGALS3BP Galectin-3-binding protein 1.83 GAALysosomal alpha-glucosidase 1.83 ISCU Iron-sulfur cluster assemblyenzyme ISCU, mitochondrial 1.83 GALNS N-acetylgalactosamine-6-sulfatase1.82 DECR2 Peroxisomal 2,4-dienoyl-CoA reductase 1.81 ABAT4-aminobutyrate aminotransferase 

  mitochondrial 1.81 PALM3 Paralemmin-3 1.81 ABCB6 ATP-binding cassettesub-family B member 6, mitochondrial 1.80 GFER FAD-linked sulfhydryloxidase ALR 1.80 CD59 CD59 glycoprotein 1.80 SLC39A11 Zinc transporterZIP11 1.80 CAPN2 Calpain-2 catalytic subunit 1.79 FAM174B Membraneprotein FAM174B 1.79 TMEM160 Transmembrane protein 160 1.79 ACADSBShort/branched chain specific acyl-CoA dehydrogenase, mitochondrial 1.79FAM8A1 Protein FAM8A1 1.79 CAPS Calcyphosin 1.79 ARMC10 Armadillorepeat-containing protein 10 1.78 TMTC3 Transmembrane and TPRrepeat-containing protein 3 1.78 SCFD2 Sec1 family domain-containingprotein 2 1.78 HDHD3 Haloacid dehalogenase-like hydrolasedomain-containing protein 3 1.78 RETSAT All-trans-retinol13,14-reductase 1.77 COQ9 Ubiquinone biosynthesis protein COQ9,mitochondrial 1.77 SPATA20 Spermatogenesis-associated protein 20 1.77EML2 Echinoderm microtubule-associated protein-like 2 1.77 ALDH5A1Succinate-semialdehyde dehydrogenase, mitochondrial 1.76 GRN Granulins1.76 CPT2 Carnitine O-palmitoyltransferase 2, mitochondrial 1.76 PEX11BPeroxisomal membrane protein PEX11B 1.76 HMGCL Hydroxymethylglutaryl-CoAlyase, mitochondrial 1.75 GSTK1 Glutathione S-transferase kappa 1 1.75DHRS7B Dehydrogenase/reductase SDR family member 7B 1.75 FDXRNADPH:adrenodoxin oxidoreductase, mitochondrial 1.75 EPS8L1 Epidermalgrowth factor receptor kinase substrate 8-like protein 1 1.74 SLC22A18Solute carrier family 22 member 18 1.74 CYCS Cytochrome e 1.74 MAPRE3Microtubule-associated protein RP/EB family member 3 1.74 SQORSulfide:quinone oxidoreductase, mitochondrial 1.73 PDIA5 Proteindisulfide-isomerase A5 1.73 HIGD1C HIG1 domain family member 1C 1.72EML3 Echinoderm microtubule-associated protein-like 3 1.72 PCLAEPCNA-associated factor 1.72 ATP6V0A1 V-type proton ATPase 116 kDasubunit a isoform 1 1.71 TAOK3 Serine/threonine-protein kinase TAO3 1.71ITGAV Integrin alpha-V 1.71 CAMK2D Calcium/calmodulin-dependent proteinkinase type II subunit delta 1.70 SLC9A1 Sodium/hydrogen exchanger 11.69 CALML5 Calmodulin-like protein 5 1.69 HMOX1 Heme oxygenase 1 1.69RNASET2 Ribonuclease T2 1.69 SELENBP1 Methanethiol oxidase 1.68 ACAA13-ketoacyl-CoA thiolase, peroxisomal 1.68 FKBP11 Peptidyl-prolylcis-trans isomerase FKBP11 1.68 RRM2B Ribonucleoside-diphosphatereductase subunit M2 B 1.68 MLYCD Malonyl-CoA decarboxylase,mitochondrial 1.67 ENDOG Endonuclease G, mitochondrial 1.67 HPDL4-hydroxyphenylpyruvate dioxygenase-like protein 1.67 CYB5R1NADH-cytochrome b5 reductase 1 1.66 KIF1A Kinesin-like protein KIF1A1.66 ENTPD8 Ectonucicoside triphosphate diphosphohydrolase 8 1.66 DLGAP4Disks large-associated protein 4 1.66 IVD Isovaleryl-CoA dehydrogenase,mitochondrial 1.66 MRPS18C 28S ribosomal protein S18c, mitochondrial1.66 CTSD Cathepsin D 1.66 HIBCH 3-hydroxyisobutyryl-CoA hydrolase,mitochondrial 1.66 HS1BP3 HCLS1-binding protein 3 1.66 MISP Mitoticinteractor and substrate of PLK1 1.66 ANXA2 Annexin A2 1.65 CD44 CD44antigen 1.65 MSRB2 Methionine-R-sulfoxide reductase B2, mitochondrial1.65 GLB1 Beta-galactosidase 1.64 CPD Carboxypeptidase D 1.64 TACSTD2Tumor-associated calcium signal transducer 2 1.64 COMTD1 CatecholO-methyltransferase domain-containing protein 1 1.64 RIN1 Ras and Rabinteractor 1 1.63 CMAS N-acylneuraminate cytidylyltransfcrasc 1.63 NQO1NAD(P)H dehydrogenase [quinone] 1 1.63 ERLEC1 Endoplasmic reticulumlectin 1 1.63 CDS2 Phosphatidate cytidylyltransferase 2 1.63 GLUD2Glutamate dehydrogenase 2, mitochondrial 1.62 VDAC1 Voltage-dependentanion-selective channel protein 1 1.61 TTC19 Tetratricopeptide repeatprotein 19, mitochondrial 1.61 SEMA3C Semaphorin-3C 1.61 LRSAM1 E3ubiquitin-protein ligase LRSAM1 1.60 ACOT13 Acyl-coenzyme A thioesterase13 1.60 LXN Latexin 1.60 GSN Gelsolin 1.60 CHP1 Calcineurin B homologousprotein 1 1.60 GALNT2 N-acetylgalactosaminyltransferase 2 1.60 RARS2Arginine-tRNA ligase, mitochondrial 1.60 PACS1 Phosphofurin acidiccluster sorting protein 1 1.60 RMDN3 Regulator of microtubule dynamicsprotein 3 1.60 PANK4 Pantothenate kinase 4 1.59 KTN1 Kinectin 1.59 CTSBCathepsin B 1.58 BCKDHA 2-oxoisovalerate dehydrogenase subunit alpha,mitochondrial 1.58 EBAG9 Receptor-binding cancer antigen expressed onSiSo cells 1.58 TMEM214 Transmembrane protein 214 1.58 UQCC2Ubiquinol-cytochrome-c reductase complex assembly factor 2,mitochondrial 1.58 TM9SF4 Transmembrane 9 superfamily member 4 1.58HDHD2 Haloacid dehalogenase-like hydrolase domain-containing protein 21.58 EPHX1 Epoxide hydrolase 1 1.58 TMF1 TATA element modulatory factor1.58 CDIPT CDP-diacylglyecrol-inositol 3-phosphatidyltransferase 1.57CD81 CD81 antigen 1.57 SRXN1 Sulfiredoxin-1 1.57 ME1 NADP-dependentmalic enzyme 1.57 ACOT8 Acyl-coenzyme A thioesterase 8, peroxisomal 1.57SMDT1 Essential MCU regulator, mitochondrial 1.56 ALG1Chitobiosyldiphosphodolichol beta-mannosyltransferase 1.56 DNAJC5 DnaJhomolog subfamily C member 5 1.55 DBT Lipoamide acyltransferasecomponent of branched-chain alpha-keto acid 1.55 dehydrogenase complex,mitochondrial LAMTOR2 Ragulator complex protein LAMTOR2 1.54 TIGARFructose-2,6-bisphosphatase TIGAR 1.54 IDUA Alpha-L-iduronidase 1.54TMEM87B Transmembrane protein 87B 1.54 TNKS1BP1 182 kDatankyrase-1-binding protein 1.54 MIA3 Transport and Golgi organizationprotein 1 homolog 1.54 TXNRD1 Thioredoxin reductase 1, cytoplasmic 1.54MYOF Myoferlin 1.54 RABEP2 Rab GTPase-binding effector protein 2 1.53GLUD1 Glutamate dehydrogenase 1, mitochondrial 1.53 PDF Peptidedeformylase, mitochondrial 1.53 TAPBP Tapasin 1.53 NDUFS7 NADHdehydrogenase [ubiquinone] iron-sulfur protein 7, mitochondrial 1.53ATP2C1 Calcium-transporting ATPase type 2C member 1 1.53 ANK3 Ankyrin-31.53 ABHD11 Protein ABHD11 1.53 AGO3 Protein argonaute-3 1.53 S100A16Protein S100-A16 1.53 TM7SF2 Delta(14)-sterol reductase 1.53 MRPL21 39Sribosomal protein L21, mitochondrial 1.53 RAB9A Ras-related proteinRab-9A 1.53 TOM1 Target of Myb protein 1 1.53 C21orf33 ES1 proteinhomolog, mitochondrial 1.52 SURF1 Surfeit locus 1 (cytochrome c oxidaseassembly protein), mitochondrial 1.52 NAMPT Nicotinamidephosphoribosyltransferase 1.51 METTL7B Methyltransferase-like protein 7B1.51 CTSA Cathepsin A 1.51 TTC37 Tetratricopeptide repeat protein 371.51 RIDA 2-iminobutanoate/2-iminopropanoate deaminase 1.50 ARPC1AActin-related protein 2/3 complex subunit 1A 1.50 OS9 Protein OS-9 1.50FUCA1 Tissue alpha-L-fucosidase 1.50

indicates data missing or illegible when filed

TABLE 5 Mitochondrial-related Proteins Up-regulated in e-CSCs, derivedfrom MCF7 3D-Spheroids Fold-Change Symbol Gene Description(Up-regulation) GLRX Glutaredoxin-1 10.79 ALDH3A1 Aldehydedehydrogenase, dimeric NADP-preferring 10.24 QPRT Nicotinate-nucleotidepyrophosphorylase [carboxylating] 3.72 MIC13 MICOS complex subunitMIC13, mitochondrial 2.36 OXSM 3-oxoacyl-[acyl-carrier-protein]synthase, mitochondrial 2.22 FECH Ferrochelatase, mitochondrial 2.16 MCUCalcium uniporter protein, mitochondrial 2.13 GCLC Glutamate-cysteineligase catalytic subunit 2.01 NQO2 Ribosyldihydronicotinamidedehydrogenase [quinone] 1.90 CMC1 COX assembly mitochondrial proteinhomolog 1.87 ISCU Iron-sulfur cluster assembly enzyme ISCU,mitochondrial 1.83 ABAT 4-aminobutyrate aminotransferase, mitochondrial1.81 ABCB6 ATP-binding cassette sub-family B member 6, mitochondrial1.80 ACADSB Short/branched chain specific acyl-CoA dehydrogenase, 1.79mitochondrial COQ9 Ubiquinone biosynthesis protein COQ9, mitochondrial1.77 ALDH5A1 Succinate-semialdehyde dehydrogenase, mitochondrial 1.76CPT2 Carnitine O-palmitoyltransferase 2, mitochondrial 1.76 HMGCLHydroxymethylglutaryl-CoA lyase, mitochondrial 1.75 FDXRNADPH:adrenodoxin oxidoreductase, mitochondrial 1.75 CYCS Cytochrome c1.74 SQOR Sulfide:quinone oxidoreductase, mitochondrial 1.73 HMOX1 Hemeoxygenase 1 1.69 MLYCD Malonyl-CoA decarboxylase, mitochondrial 1.67ENDOG Endonuclease G, mitochondrial 1.67 IVD Isovaleryl-CoAdehydrogenase, mitochondrial 1.66 MRPS18C 28S ribosomal protein S18c,mitochondrial 1.66 HIBCH 3-hydroxyisobutyryl-CoA hydrolase,mitochondrial 1.66 MSRB2 Methionine-R-sulfoxide reductase B2,mitochondrial 1.64 NQO1 NAD(P)H dehydrogenase [quinone] 1 1.63 GLUD2Glutamate dehydrogenase 2, mitochondrial 1.62 VDAC1 Voltage-dependentanion-selective channel protein 1 1.61 TTC19 Tetratricopeptide repeatprotein 19, mitochondrial 1.61 ACOT13 Acyl-coenzyme A thioesterase 13,mitochondrial 1.60 RARS2 Arginine-tRNA ligase, mitochondrial 1.60 BCKDHA2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial 1.58 UQCC2Ubiquinol-cytochrome-c reductase complex assembly factor 2, 1.58Mitochondrial ME1 NADP-dependent malic enzyme 1.57 SMDT1 Essential MCUregulator, mitochondrial 1.56 DNAJC5 DnaJ homolog subfamily C member 51.55 DBT Lipoamide acyltransferase/branched-chain α-keto dehydrogenase,1.55 mitochondrial TIGAR Fructose-2,6-bisphosphatase TIGAR 1.54 GLUD1Glutamate dehydrogenase 1, mitochondrial 1.53 PDF Peptide deformylase,mitochondrial 1.53 NDUFS7 NADU dehydrogenase [ubiquinone] iron-sulfurprotein 7, 1.53 Mitochondrial MRPL21 39S ribosomal protein L21,mitochondrial 1.53 C21orf33 ES1 protein homolog, mitochondrial 1.52SURF1 Surfeit locus 1 (cytochrome c oxidase assembly protein), 1.52Mitochondrial NAMPT Nicotinamide phosphoribosyltransferase 1.51

TABLE 6 Functional Markers of the e-CSC Phenotype (from MCF73D-Spheroids) Fold-Change Symbol Gene Description (Up-regulation)Senescence Markers CDKN1A Cyclin-dependent kinase inhibitor 1(p21-WAF/CDK-inhihilor) 17.22  GLB1 Beta-galactosidase 1.64 Anti-OxidantResponse to ROS/Oxidative Stress GLRX Glutaredoxin-1 10.79  GCLCGlutamate-cysteine ligase catalytic subunit 2.01 NQO2Ribosyldihydronicotinamide dehydrogenase [quinone] 1.90 MGST1 Microsomalglutathione S-transferase 1 1.88 MGST3 Microsomal glutathioneS-transferase 3 1.84 SPATA20 Spermatogenesis-associated protein 20(thioredoxin-like) 1.77 GSTK1 Glutathione S-transfcrasc kappa 1 1.75NQO1 NAD(P)H dehydrogenase [quinone] 1 1.63 Stemness &Drug-Resistance/Radio-Resistance BCAS1 Breast carcinoma-amplifiedsequence 1 119.37  ALDH3A1 Aldehyde dehydrogenase, dimericNADP-preferring 10.24  CEACAM6 Carcinoembryonic antigen-related celladhesion molecule 6 9.66 CEACAM5 Carcinoembryonic antigen-related celladhesion molecule 5 3.38 LAMA5 Laminin subunit alpha-5 2 72 ALDH5A1Succinate-semialdehyde dehydrogenase, mitochondrial 1.76 CD44 CD44antigen 1.65 Cytoskeletal Proteins (indicative of an EMT in CSCs) TUBA4ATubulin alpha-4A chain 2.60 STOM Erythrocyte band 7 integral membraneprotein 2.18 MAPRE3 Microtubule-associated protein RP/EB family member 31.74 KIF1A Kinesin-like protein KIF1A 1.66 RMDN3 Regulator ofmicrotubule dynamics protein 3 1.60 GSN Gelsolin 1.60 MYOF Myoferlin1.54 ANK3 Ankyrin-3 1.53 ARPC1A Actin-related protein 2/3 complexsubunit 1A 1.50 Spindle Orientation and Mitotic Progression MISP Mitoticinteractor and substrate of PLK1 1.66 Mitochondrial Biogenesis GLRXGlutaredoxin-1 10.79  MIC13 MICOS complex subunit MIC13, mitochondrial2.36 OXSM 3-oxoacyl-[acyl-carrier-protein] synthase, mitochondrial 2.22FECH Ferrochelatase, mitochondrial 2.16 CMC1 COX assembly mitochondrialprotein homolog 1.87 ISCU Iron-sulfur cluster assembly enzyme ISCU,mitochondrial 1.83 COQ9 Ubiquinone biosynthesis protein COQ9,mitochondrial 1.77 HMOX1 Heme oxygenase 1 1.69 UQCC2Ubiquinol-cytochrome-c reductase complex assembly factor 2, 1.58Mitochondrial MRPS18C 28S ribosomal protein S18c, mitochondrial 1.66RARS2 Arginine-tRNA ligase, mitochondrial 1.60 MRPL21 39S ribosomalprotein L21, mitochondrial 1.53 PDF Peptide deformylase, mitochondrial1.53 Glutamine/Asparagine Metabolism AGAN(4)-(beta-N-acctylglucosaminyl)-L-asparaginasc 2.30 GLUD2 Glutamatedehydrogenase 2, mitochondrial 1.62 GLUD1 Glutamate dehydrogenase 1,mitochondrial 1.53 NADH/NADPH: Synthesis & Salvage Pathway ALDH3A1Aldehyde dehydrogenase, dimeric NADP-preferring 10.24  QPRTNicotinate-nucleotide pyrophosphorylase [carboxylating] 3.72 RRM2Ribonucleoside-diphosphate reductase subunit M2 2.34 ALDH5A1Succinate-semialdehyde dehydrogenase, mitochondrial 1.76 FDXRNADPH:adrenodoxin oxidoreductase, mitochondrial 1.75 RRM2BRibonucleoside-diphosphate reductase subunit M2 B 1.68 ME1NADP-dependent malic enzyme 1.57 TIGAR Fructosc-2,6-bisphosphatasc TIGAR1.54 TNKS1BP1 182 kDa tankyrase-1-binding protein 1.54 NDUFS7 NADHdehydrogenase [ubiquinone] iron-sulfur protein 7, 1.53 mitochondrialNAMPT Nicotinamide phosphoribosyltransferase 1.51 Flavin-containingEnzymes CYP1A1 Cytochrome P450 1A1 6.60 MAOA Amine oxidase[flavin-containing] A 4.73 MAOB Amine oxidase [flavin-containing) B 2.45GFER FAD-linked sulfhydryl oxidase ALR 1.80 CYB5R1 NADH-cytochrome b5reductase 1 1.66 TXNRD1 Thioredoxin reductase 1, cytoplasmic(Glutaredoxin activity; 1.54 flavin-dependent) Epithelial Markers KRT10Keratin, type 1 cytoskeletal 10 4.59 DPP7 Dipeplidyl peptidase 2 2.43MUC5B Mucin-5B 1.98 Cell Surface Markers GM2A Ganglioside GM2 activator2.36 CD59 CD59 glycoprotein 1.80 ENTPD8 Ectonucleoside triphosphatediphosphohydrolase 8 1.66 CD81 CD81 antigen 1.57 S100 Proteins S100PProtein S100-P 2.11 S100A10 Protein S100-A10 1.96 S100A16 ProteinS100-A16 1.53 Autophagy/Lysosomes CHMP6 Charged multivesicular bodyprotein 6 2.62 SERPINA3 Alpha-1-antichymotrypsin 2.33 CTSH Cathepsin H1.98 GNS N-acetylglucosamine-6-sulfatase 1.97 GAA Lysosomalalpha-glucosidase 1.83 GALNS N-acetylgalactosamine-6-sulfatase 1.82ATP6V0A1 V-type proton ATPase 116 kDa subunit a isoform 1 1.71 CTSDCathepsin D 1.66 CPD Carboxypeptidase D 1.64 GALNT2N-acetylgalactosaminyltransferase 2 1.60 CTSB Cathepsin B 1.58 CTSACathepsin A 1.51 Peroxisomes PEX14 Peroxisomal membrane protein PEX141.91 DECR2 Peroxisomal 2,4-dienoyl-CoA reductase 1.81 PEX11B Peroxisomalmembrane protein PEX11B 1.76 ACOT8 Acyl-coenzyme A thioesterase 8,peroxisomal 1.57 RABs RAB27B Ras-related protein Rab-27B 2.69 RAB27ARas-related protein Rab-27A 2.54 RAB24 Ras-related protein Rab-24 1.87RIN1 Ras and Rab interactor 1 1.63 RABEP2 Rab GTPase-binding effectorprotein 2 1.53 RAB9A Ras-related protein Rab-9A 1.53 Annexins and PARPANXA1 Annexin A1 2.87 PARP4 Poly [ADP-ribose] polymerase 4 2.55 ANXA2Annexin A2 1.65 Calcium/Calmodulin CIB1 Calcium and integrin-bindingprotein 1 2.90 MCE Calcium uniporter protein, mitochondrial 2.13 CAPSCalcyphosin 1.79 CAMK2D Calcium/calmodulin-dependent protein kinase,type II 1.70 subunit delta CALML5 Calmodulin-like protein 5 1.69 TACSTD2Tumor-associated calcium signal transducer 2 1.64 CHP1 Calcineurin Bhomologous protein 1 1.60 SMDT1 Essential MCU regulator, mitochondrial1.56 ATP2C1 Calcium-transporting ATPase type 2C member 1 1.53

TABLE 7 eCSC Marker Proteins are Transcriptionally Up-regulated inPatient- derived Human Breast Cancer Cells in Vivo Fold- Symbol GeneDescription Change P-value TSPAN31 Tetraspanin-31 4.72 8.45E−06 CDS2Phosphatidate cytidylyltransferase 2 4.71 8.73E−06 PEX11B Peroxisomalmembrane protein PEX11B 4.69 9.58E−06 RAB9A Ras-related protein Rab-9A4.47 2.02E−05 TACSTD2 Tumor-associated calcium signal 4.41 2.47E−05transducer 2 GLUD1 Glutamate dehydrogenase 1, 4.38 2.76E−05mitochondrial MSRB2 Methionine-R-sulfoxide reductase 4.31 3.49E−05 B2,mitochondrial SURF1 Surfeit locus 1 (cytochrome c oxidase 4.16 5.66E−05assembly protein), mitochondrial PON2 Serum paraoxonase/arylesterase 24.01 9.25E−05 CYB5R1 NADH-cytochrome b5 reductase 1 3.94 1.18E−04 ANK3Ankyrin-3 3.81 1.77E−04 ASAH1 Acid ceramidase 3.80 1.83E−04 CD59 CD59glycoprotein 3.60 3.47E−04 OXSM 3-oxoacyl-[acyl-carrier-protein] 3.494.82E−04 synthase, mitochondrial NQO1 NAD(P)H dehydrogenase [quinone] 13.49 4.81E−04 SEMA3C Semaphorin-3C 3.49 4.92E−04 CD44 CD44 antigen 3.445.69E−04 ALDH5A1 Succinate-semialdehyde dehydrogenase, 3.43 5.75E−04mitochondrial AGA N(4)-(beta-N-acetylglucosaminyl)-L- 3.40 6.30E−04asparaginase GSTK1 Glutathione S-transferase kappa 1 3.39 6.59E−04 KTN1Kinectin 3.36 7.16E−04 FECH Ferrochelatase, mitochondrial 3.36 7.20E−04C21orf33 ES1 protein homolog, mitochondrial 3.31 8.40E−04 MPV17 ProteinMpv17 3.27 9.44E−04 TMEM214 Transmembrane protein 214 3.12 1.44E−03 NEBLNebulette 3.09 1.59E−03 CDIPT CDP-diacylglycerol-inositol 3- 3.061.74E−03 phosphatidyltransferase CPT2 Carnitine O-palmitoyltransferase2, 3.02 1.94E−03 mitochondrial ATP2C1 Calcium-transporting ATPase type2C 3.01 1.96E−03 member 1 SERPINA3 Alpha-1-antichymotrypsin 2.992.11E−03 CYCS Cytochrome c 2.92 2.52E−03 TTC19 Tetratricopeptide repeatprotein 19, 2.85 3.06E−03 mitochondrial SELENBP1 Methanethiol oxidase2.84 3.22E−03 MIA3 Transport and Golgi organization 2.76 3.98E−03protein 1 homolog OS9 Protein OS-9; amplified in osteosarcoma 2.763.99E−03 ANXA2 Annexin A2 2.73 4.30E−03 SULT1A1 Sulfotransferase 1A12.72 4.34E−03 MYOF Myoferlin 2.67 5.00E−03 CAPN2 Calpain-2 catalyticsubunit 2.64 5.42E−03 VDAC1 Voltage-dependent anion-selective 2.645.35E−03 channel protein 1, mitochondrial TXNRD1 Thioredoxin reductase1, cytoplasmic 2.64 5.36E−03 EPS8L1 Epidermal growth factor receptorkinase 2.57 6.54E−03 substrate 8-like protein 1 PDF Peptide deformylase,mitochondrial 2.56 6.71E−03 CTSH Cathepsin H 2.54 7.07E−03 KRT10Keratin, type  

  cytoskeletal 10 2.53 7.19E−03 GLB1 Beta-galactosidase 2.53 7.20E−03GM2A Ganglioside GM2 activator 2.42 9.42E−03 RRM2Ribonucleoside-diphosphate reductase 2.40 9.93E−03 subunit M2 RETSATAll-trans-retinol 13,14-reductase 2.39 1.03E−02 RNASET2 Ribonuclease T22.36 1.10E−02 ENDOG Endonuclease G, mitochondrial 2.32 1.22E−02 NAMPTNicotinamide phosphoribosyltransferase 2.19 1.66E−02 SPATA20Spermatogenesis-associated protein 20 2.16 1.77E−02 SLC22A18 Solutecarrier family 22 member 18 2.14 1.86E−02 ABAT 4-aminobutyrateaminotransferase, 2.08 2.14E−02 mitochondrial TAPBP Tapasin 2.082.13E−02 CIB1 Calcium and integrin-binding protein 1 2.04 2.34E−02 HMGCLHydroxymethylglutaryl-CoA lyase, 2.03 2.38E−02 mitochondrial FAMSA1Protein FAM8A1 2.02 2.40E−02 GCLC Glutamate-cysteine ligase catalytic2.01 2.49E−02 subunit ACAA1 3-ketoacyl-CoA thiolase, peroxisomal 2.002.53E−02 GLRX Glutaredoxin-1 1.92 3.01E−02 ISCU Iron-sulfur clusterassembly enzyme 1.92 3.02E−02 ISCU, mitochondrial TMF1 TATA elementmodulatory factor 1.88 3.25E−02 CD81 CD81 antigen 1.87 3.34E−02 NQO2Ribosyldihydronicotinamide 1.79 3.98E−02 dehydrogenase [quinone] MAOBAmine oxidase [flavin-containing] B 1.74 4.41E−02 CEACAM6Carcinoembryonic antigen-related cell 1.70 4.71E−02 adhesion molecule 6SLC9A1 Sodium/hydrogen exchanger 1 1.68 4.97E−02

indicates data missing or illegible when filed

TABLE 8 Tumor Recurrence (RFS): Predicting Tamoxifen-resistance in ER(+)Breast Cancer Patients Gene Probe Gene Symbol HR (Hazard-Ratio) Log-RankTest 201468_s_at NQO1 2.47 0.0023 203608_at ALDH5A1 2.21 0.01 201266_atTXNR 2.17 0.0062 201890_at RRM2 2.54 0.00089 Combined Signature (RFS)3.89 4.1e−05 RFS, recurrence-free survival.

TABLE 9 Distant Metastasis (DMFS): Predicting Tamoxifen- resistance inER(+) Breast Cancer Patients Gene Probe Gene Symbol HR (Hazard-Ratio)Log-Rank Test 201468_s_at NQO1 1.73 0.1 203608_at ALDH5A1 2.86 0.0034201266_at TXNR 3.64 0.00035 201890_at RRM2 3.02 0.00092 DMFS, distantmetastasis-free survival.

1. A method for predicting and treating tamoxifen resistance in a breastcancer, the method comprising: obtaining a biological sample of thecancer; determining, or having determined, a level of expression of eachmember of an e-CSC gene signature comprising aldehyde dehydrogenase 5family member A1 (ALDH5A1), thioredoxin reductase 1 (TXNR), andribonucleotide-diphosphate reductase subunit M2 (RRM2); comparing thedetermined level to a threshold level from a healthy cell line for eachmember of the e-CSC gene signature; administering a pharmaceuticallyeffective amount of at least one of an oxidative phosphorylation(OXPHOS) inhibitor and a cyclin-dependent kinase 4/6 (CDK4/6) inhibitorif the determined level exceeds the threshold level.
 2. The method ofclaim 1, wherein the gene signature further comprises NAD(P)H quinonedehydrogenase 1 (NQO1).
 3. The method of claim 1, wherein the at leastone of an OXPHOS inhibitor and a CDK4/6 inhibitor comprisesdiphenyleneiodonium chloride (DPI).
 4. The method of claim 1, whereinthe at least one of an OXPHOS inhibitor and a CDK4/6 inhibitor comprisesRibociclib.
 5. The method of claim 1, wherein the at least one of anOXPHOS inhibitor and a CDK4/6 inhibitor comprises at least one of DPI,atovaquone, irinotecan, sorafenib, niclosamide, berberine chloride,Ribociclib, Abemaciclib, and Palbociclib.
 6. The method of claim 1,wherein the at least one of an OXPHOS inhibitor and a CDK4/6 inhibitorcomprises the combination of at least one OXPHOS inhibitor and at leastone CDK4/6 inhibitor.
 7. The method of claim 1, wherein thepharmaceutically effective amount of at least one of an OXPHOS inhibitorand a CDK4/6 inhibitor is administered if the quotient of the determinedlevel divided by the threshold level is greater than 1.2.
 8. A methodfor predicting and treating tamoxifen resistance in a breast cancer, themethod comprising: obtaining a biological sample of the cancer;determining, or having determined, a level of expression of each memberof an e-CSC gene signature comprising NAD(P)H quinone dehydrogenase 1(NQO1), aldehyde dehydrogenase 5 family member A1 (ALDH5A1), thioredoxinreductase 1 (TXNR), and ribonucleotide-diphosphate reductase subunit M2(RRM2); comparing the determined level to a threshold level from ahealthy cell line for each member of the e-CSC gene signature;administering a pharmaceutically effective amount of at least one of anoxidative phosphorylation (OXPHOS) inhibitor and a cyclin-dependentkinase 4/6 (CDK4/6) inhibitor if the determined level exceeds thethreshold level.